Learning framework for NNs. Introduction to Neural Networks. Learning goal: Inputs/outputs. x 1 x 2. y 1 y 2
|
|
|
- Isaac Hunt
- 7 years ago
- Views:
Transcription
1 Introduction to Neura Networks Learning framework for NNs What are neura networks? Noninear function approimators How do they reate to pattern recognition/cassification? Noninear discriminant functions More compe decision boundaries than inear discriminant functions (e.g. Fisher, Gaussians with equa covariances inputs n Unknown mapping G rainabe mode Γ y y y m m mode outputs desired outputs Inputs/outputs Learning goa: Definitions: y y G( (e.g discriminant function we want to earn n ( n inputs y y y m ( m process outputs Find w such that: E( w E( w, w, where E( w error between G and Γ. rainabe mode: Γ(, ( w adjustabe parameters w m ( m mode outputs What shoud E( w be?
2 Error function (idea Error function (practica Input/output data: p input-output training patterns Ideay, E( w y p( d p y y y p How to compute? i y i i i in y i y i y im, i Γ( i, w. E( w p -- y i i i p m -- y ( ij ij i j Artificia neura networks (NNs Neura networks are one type of parametric mode Γ. Noninear function approimators Bioogica inspiration Structure and function oosey based on bioogica neura networks (e.g. brain. Reativey simpe buiding bocks connected together in massive and parae network. Adjustabe (trainabe parameters w (weights Map inputs to outputs dendrites neuron aon Why Neura Network? What does a neuron do?
3 Neuron transfer function Rough approimation: threshod function aon output net stimuus from dendrites Neura networks: crude emuation of bioogy Simpe basic buiding bocks. Individua units are connected massivey and in parae. Individua units have threshod-type activation functions. Learning through adjustment of the strength of connection (weights between individua units Caveat: Artificia neura networks are much, much, much simper than bioogica systems. Eampe: Human brain: neurons connections Basic buiding bocks of neura networks Basic buiding bock: the unit ψ φ φ φ φ q (scaar inputs w ω ω ω q (weights ω ω ω ω q φ φ φ φ q noninear activation function ψ ( w ω i φ i φ q i (output
4 Perceptrons: the simpest neura network hreshod activation function t ( u u θ u < θ ω ω n ω ω n t ( u What is this? θ u Perceptron output Limited mapping capabiity Perceptron mapping: w t w t < ω.5 ω ω OR function where,.8 n.6.4. w ω ω ω n XOR function
5 More genera networks: activation function ( u ( u ( u (sigmoid + e u e u e u e u e u (hyperboic tangent + sigmoid ( u hyperboic tangent signa fow (feedforward More genera networks: mutiayer perceptrons (MLPs m n output ayer input ayer hidden unit ayer u u signa fow (feedforward More genera networks: mutiayer perceptrons (MLPs m n output ayer hidden unit ayer # hidden unit ayer # input ayer MLP appication eampe: ALVINN Sharp Left Straight Ahead Sharp Right 4 Hidden Units 3 Output Units 33 Sensor Input Retina ALVINN: Neura Network for Autonomous Steering
6 A simpe eampe Derivation of function ƒ( f ( c[ t ( a t ( b ] ω 6 ω 7 f ( c t ( a c t ( b ω 5 f ( c ω ω ω 4 ω 3 t ( u ( ku as k f ( c [ k ( a ] c [ k ( b ] for arge k. a b ω 5 + ω 6 ( ω + ω + ω 7 ( ω 3 + ω 4 Weight vaues for simpe eampe Some theoretica properties of NNs set # set # ω ω ω 3 ω 4 ω 5 ω 6 ω 7 kb k ka k c c ka k kb k c c Singe-input functions: what does the previous eampe say about singe-input functions? f ( NN error
7 Muti-input functions: universa function approimator? Does the singe-input eampe hod in genera? Neura networks in practice: 3 basic steps. Coect input/output training data.. Seect an appropriate neura network architecture: Number of hidden ayers Number of hidden units in each ayer. 3. rain (adjust the weights of the neura network to minimie the error measure, E p -- y i i i Neura network training Gradient descent (one parameter Key probem: How to adjust w to minimie E? Answer: use derivative information on error surface.. Initiaie ω to some random initia vaue.. Change ω iterativey at step t according to: E ( ω a b e c ω( t + ω( t η de dω( t d f ω Impies oca, not goba minimum...
8 Genera gradient descent Simpe eampe of gradient computation. Initiaie w to some random initia vaue.. Change w iterativey at step t according to: w( t + w( t η E[ w( t ] E[ w( t ] ω ( t ω ( t ( ωq t Compute ω 4 ω 5 for the neura network beow: ω 6 ω 7 ω ω ω 4 Singe training pattern y, E -- ( y ω 3 Derivation Generaiation to mutipe training patterns: ω j p -- y ω j ( i i i p i -- ( y i i ω j. net ω + ω net ω 3 + ω 4 h ( net h ( net ω 5 + ω 6 h + ω 7 h ω 4 ( y ω4 Derivation ω 5 ω ω ω 4 ω 3 ω 6 ω 7 ω 4 h ( y h net net ω 4
9 net ω + ω net ω 3 + ω 4 h ( net h ( net ω 5 + ω 6 h + ω 7 h ω 4 ω 4 Derivation ω 5 h ( y h net net ω 4 ( yω 7 ' ( net ω ω ω 4 ω 3 ω 6 ω 7 Generaiation: Backpropagation Key probem: Generaie specific resut to compute derivatives in more genera manner. Answer: Backpropagation agorithm [Rumehart and McCeand,986]. Efficient, agorithmic formuation for computing error derivatives Gradient computation without hardcoding derivatives (aows on-the-fy adjustment of NN architectures. ( h k ω kj ω ij k Backpropagation derivation net j ω ij h i ω ij h i ω ij ω ij h i Backpropagation derivation: output units net k ω jk h i unit k ω ij E net k m -- y ( k net k
10 Backpropagation derivation: output units unit k m E -- y ( Backpropagation derivation:output units unit k net k k net k net k ω jk net k k net k net k ω jk ( net k h i ω ij ' ( net net k k h i ω ij ( y k '( net k ( y k ω jk Backpropagation derivation: hidden units unit k Backpropagation derivation: hidden units unit k net k ω jk net k ω jk h i ω ij net net h i ω ij net net net δ
11 Backpropagation derivation: hidden units net k ω jk net unit k δ net s ω s ( net s Backpropagation derivation: hidden units net k ω jk unit k h i ω ij net ω ' ( j δ ω j ' ( h i ω ij δ ω j ' ( netj ω ij h i Output units: ω jk Hidden units: Backpropagation summary ( y k '( net k Basic steps in using neura networks. Coect training data. Preprocess training data 3. Seect neura network architecture 4. Seect earning agorithm 5. Weight initiaiation ω ij h i 6. Forward pass δ ω j ' ( netj 7. Backward pass 8. Repeat steps 6 and 7 unti satisfactory mode is reached.
12 he Forward Pass he Backward Pass. Evauate at the outputs, where,. Appy an input vector to network.. Compute the net input to each hidden unit ( netj. 3. Compute the hidden-unit outputs (, 4. Compute the neura network outputs (. i net k for each output unit k.. Backpropagate the δ vaues from the outputs backwards through the neura network. 3. Compute. ω i 4. Update weights based on the computed gradient, w( t + w( t η E[ w( t ]. Practica issues. What shoud your training data be? Practica issues (continued 5. Seecting the earning parameter Sufficient training data? Biased training data? Deterministic/stochastic task? Stationary/non-stationary? In gradient descent: w( t + w( t η E[ w( t ] what shoud η be?. What shoud your neura network architecture be? 3. Preprocessing of data. 4. Weight initiaiation why sma, random vaues? Difficut question to answer...
13 Seecting the earning parameter: an eampe Sampe error surface: E ω + ω (reaistic? Seecting the earning parameter: an eampe Where is the minimum of this error surface? E ω + ω E ω -.5 ω How many steps to convergence? ( E < 6 Different initia weights Different earning rates Deriving the gradient descent equations Gradient? E ω + ω Convergence eperiments Initia weights: ( ω, ω (, ω ω Gradient descent? ω ( t + ω ( t η ω ( t ω ( t + ω ( t ( 4η ω ( t + ω ( t ( η ω ω # steps to convergence η
14 A coser ook A coser ook η. η ω.5 ω ω ω A coser ook η.5 What happens at η >.5? Gradient descent equations: ω ( t + ω ( t ( 4η.5 ω ( t + ω ( t ( η ω ω Simiar to fied-point iteration: ω( t + cω( t diverges for c >, ω( converges for c <.
15 Convergence of gradient descent equations Learning rate discussion ω ( t + ω ( t ( 4η require that: 4η < < 4η < < η <.5 Why not η <? ω ( t + ω ( t ( η Probematic error surfaces: ong, steep-sided vaeys If earning rate is too sma, sow convergence. If earning rate is too arge, possibe divergence. heoretica bounds not possibe in genera case (ony for specific, trivia eampe. Motivation for ooking at more advanced training agorithms doing more with the gradient information. Any thoughts? Practica issues (continued Good generaiation: wo data sets 6. Pattern vs. batch training 7. Good generaiation y y NN error cross-vaidation data training data Sufficienty constrained neura network architecture. Cross vaidation. eary stopping point training time
An Idiot s guide to Support vector machines (SVMs)
An Idiot s guide to Support vector machines (SVMs) R. Berwick, Viage Idiot SVMs: A New Generation of Learning Agorithms Pre 1980: Amost a earning methods earned inear decision surfaces. Linear earning
Face Hallucination and Recognition
Face Haucination and Recognition Xiaogang Wang and Xiaoou Tang Department of Information Engineering, The Chinese University of Hong Kong {xgwang1, xtang}@ie.cuhk.edu.hk http://mmab.ie.cuhk.edu.hk Abstract.
Simultaneous Routing and Power Allocation in CDMA Wireless Data Networks
Simutaneous Routing and Power Aocation in CDMA Wireess Data Networks Mikae Johansson *,LinXiao and Stephen Boyd * Department of Signas, Sensors and Systems Roya Institute of Technoogy, SE 00 Stockhom,
Lecture 6. Artificial Neural Networks
Lecture 6 Artificial Neural Networks 1 1 Artificial Neural Networks In this note we provide an overview of the key concepts that have led to the emergence of Artificial Neural Networks as a major paradigm
FRAME BASED TEXTURE CLASSIFICATION BY CONSIDERING VARIOUS SPATIAL NEIGHBORHOODS. Karl Skretting and John Håkon Husøy
FRAME BASED TEXTURE CLASSIFICATION BY CONSIDERING VARIOUS SPATIAL NEIGHBORHOODS Kar Skretting and John Håkon Husøy University of Stavanger, Department of Eectrica and Computer Engineering N-4036 Stavanger,
Chapter 4: Artificial Neural Networks
Chapter 4: Artificial Neural Networks CS 536: Machine Learning Littman (Wu, TA) Administration icml-03: instructional Conference on Machine Learning http://www.cs.rutgers.edu/~mlittman/courses/ml03/icml03/
ONE of the most challenging problems addressed by the
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, VOL. 44, NO. 9, SEPTEMBER 2006 2587 A Mutieve Context-Based System for Cassification of Very High Spatia Resoution Images Lorenzo Bruzzone, Senior Member,
Pricing Internet Services With Multiple Providers
Pricing Internet Services With Mutipe Providers Linhai He and Jean Warand Dept. of Eectrica Engineering and Computer Science University of Caifornia at Berkeey Berkeey, CA 94709 inhai, wr@eecs.berkeey.edu
Fast Robust Hashing. ) [7] will be re-mapped (and therefore discarded), due to the load-balancing property of hashing.
![Fast Robust Hashing. ) [7] will be re-mapped (and therefore discarded), due to the load-balancing property of hashing.](/thumbs/26/8145567.jpg "Fast Robust Hashing. ) [7] will be re-mapped (and therefore discarded), due to the load-balancing property of hashing.")
Fast Robust Hashing Manue Urueña, David Larrabeiti and Pabo Serrano Universidad Caros III de Madrid E-89 Leganés (Madrid), Spain Emai: {muruenya,darra,pabo}@it.uc3m.es Abstract As statefu fow-aware services
GREEN: An Active Queue Management Algorithm for a Self Managed Internet
: An Active Queue Management Agorithm for a Sef Managed Internet Bartek Wydrowski and Moshe Zukerman ARC Specia Research Centre for Utra-Broadband Information Networks, EEE Department, The University of
CS 688 Pattern Recognition Lecture 4. Linear Models for Classification
CS 688 Pattern Recognition Lecture 4 Linear Models for Classification Probabilistic generative models Probabilistic discriminative models 1 Generative Approach ( x ) p C k p( C k ) Ck p ( ) ( x Ck ) p(
An Introduction to Neural Networks
An Introduction to Vincent Cheung Kevin Cannons Signal & Data Compression Laboratory Electrical & Computer Engineering University of Manitoba Winnipeg, Manitoba, Canada Advisor: Dr. W. Kinsner May 27,
Lecture 7 Datalink Ethernet, Home. Datalink Layer Architectures
Lecture 7 Dataink Ethernet, Home Peter Steenkiste Schoo of Computer Science Department of Eectrica and Computer Engineering Carnegie Meon University 15-441 Networking, Spring 2004 http://www.cs.cmu.edu/~prs/15-441
Introduction to Machine Learning and Data Mining. Prof. Dr. Igor Trajkovski trajkovski@nyus.edu.mk
Introduction to Machine Learning and Data Mining Prof. Dr. Igor Trakovski trakovski@nyus.edu.mk Neural Networks 2 Neural Networks Analogy to biological neural systems, the most robust learning systems
SUCCESSFUL PREDICTION OF HORSE RACING RESULTS USING A NEURAL NETWORK
SUCCESSFUL PREDICTION OF HORSE RACING RESULTS USING A NEURAL NETWORK N M Allinson and D Merritt 1 Introduction This contribution has two main sections. The first discusses some aspects of multilayer perceptrons,
Figure 1. A Simple Centrifugal Speed Governor.
ENGINE SPEED CONTROL Peter Westead and Mark Readman, contro systems principes.co.uk ABSTRACT: This is one of a series of white papers on systems modeing, anaysis and contro, prepared by Contro Systems
Elsevier Editorial System(tm) for Computers in Biology and Medicine Manuscript Draft
Esevier Editoria System(tm) for Computers in Bioogy and Medicine Manuscript Draft Manuscript Number: CBM-D--00R1 Tite: CLASSIFICATION AND RETRIEVAL ON MACROINVERTABRATE IMAGE DATABASES Artice Type: Fu
Neural Networks and Support Vector Machines
INF5390 - Kunstig intelligens Neural Networks and Support Vector Machines Roar Fjellheim INF5390-13 Neural Networks and SVM 1 Outline Neural networks Perceptrons Neural networks Support vector machines
With the arrival of Java 2 Micro Edition (J2ME) and its industry
Knowedge-based Autonomous Agents for Pervasive Computing Using AgentLight Fernando L. Koch and John-Jues C. Meyer Utrecht University Project AgentLight is a mutiagent system-buiding framework targeting
Neural network software tool development: exploring programming language options
INEB- PSI Technical Report 2006-1 Neural network software tool development: exploring programming language options Alexandra Oliveira aao@fe.up.pt Supervisor: Professor Joaquim Marques de Sá June 2006
CONDENSATION. Prabal Talukdar. Associate Professor Department of Mechanical Engineering IIT Delhi E-mail: prabal@mech.iitd.ac.in
CONDENSATION Praba Taukdar Associate Professor Department of Mechanica Engineering IIT Dehi E-mai: praba@mech.iitd.ac.in Condensation When a vapor is exposed to a surface at a temperature beow T sat, condensation
Discounted Cash Flow Analysis (aka Engineering Economy)
Discounted Cash Fow Anaysis (aka Engineering Economy) Objective: To provide economic comparison of benefits and costs that occur over time Assumptions: Future benefits and costs can be predicted A Benefits,
A Latent Variable Pairwise Classification Model of a Clustering Ensemble
A atent Variabe Pairwise Cassification Mode of a Custering Ensembe Vadimir Berikov Soboev Institute of mathematics, Novosibirsk State University, Russia berikov@math.nsc.ru http://www.math.nsc.ru Abstract.
Normalization of Database Tables. Functional Dependency. Examples of Functional Dependencies: So Now what is Normalization? Transitive Dependencies
ISM 602 Dr. Hamid Nemati Objectives The idea Dependencies Attributes and Design Understand concepts normaization (Higher-Leve Norma Forms) Learn how to normaize tabes Understand normaization and database
GWPD 4 Measuring water levels by use of an electric tape
GWPD 4 Measuring water eves by use of an eectric tape VERSION: 2010.1 PURPOSE: To measure the depth to the water surface beow and-surface datum using the eectric tape method. Materias and Instruments 1.
INTRODUCTION TO THE FINITE ELEMENT METHOD
INTRODUCTION TO THE FINITE ELEMENT METHOD Evgen Barkanov Institute of Materias and Structures Facut of Civi Engineering Riga Technica Universit Riga, Preface Toda the finite eement method (FEM) is considered
Linear Models for Classification
Linear Models for Classification Sumeet Agarwal, EEL709 (Most figures from Bishop, PRML) Approaches to classification Discriminant function: Directly assigns each data point x to a particular class Ci
Leakage detection in water pipe networks using a Bayesian probabilistic framework
Probabiistic Engineering Mechanics 18 (2003) 315 327 www.esevier.com/ocate/probengmech Leakage detection in water pipe networks using a Bayesian probabiistic framework Z. Pouakis, D. Vaougeorgis, C. Papadimitriou*
Neural Networks in Quantitative Finance
Neural Networks in Quantitative Finance Master Thesis submitted to Prof. Dr. Wolfgang Härdle Institute for Statistics and Econometrics CASE - Center for Applied Statistics and Economics Humboldt-Universität
Feed-Forward mapping networks KAIST 바이오및뇌공학과 정재승
Feed-Forward mapping networks KAIST 바이오및뇌공학과 정재승 How much energy do we need for brain functions? Information processing: Trade-off between energy consumption and wiring cost Trade-off between energy consumption
Linear Classification. Volker Tresp Summer 2015
Linear Classification Volker Tresp Summer 2015 1 Classification Classification is the central task of pattern recognition Sensors supply information about an object: to which class do the object belong
Virtual trunk simulation
Virtua trunk simuation Samui Aato * Laboratory of Teecommunications Technoogy Hesinki University of Technoogy Sivia Giordano Laboratoire de Reseaux de Communication Ecoe Poytechnique Federae de Lausanne
eg Enterprise vs. a Big 4 Monitoring Soution: Comparing Tota Cost of Ownership Restricted Rights Legend The information contained in this document is confidentia and subject to change without notice. No
Temporal Difference Learning in the Tetris Game
Temporal Difference Learning in the Tetris Game Hans Pirnay, Slava Arabagi February 6, 2009 1 Introduction Learning to play the game Tetris has been a common challenge on a few past machine learning competitions.
Market Design & Analysis for a P2P Backup System
Market Design & Anaysis for a P2P Backup System Sven Seuken Schoo of Engineering & Appied Sciences Harvard University, Cambridge, MA seuken@eecs.harvard.edu Denis Chares, Max Chickering, Sidd Puri Microsoft
Applying graph theory to automatic vehicle tracking by remote sensing
0 0 Appying graph theory to automatic vehice tracking by remote sensing *Caros Lima Azevedo Nationa Laboratory for Civi Engineering Department of Transportation Av. Do Brasi, Lisbon, 00-0 Portuga Phone:
A Supplier Evaluation System for Automotive Industry According To Iso/Ts 16949 Requirements
A Suppier Evauation System for Automotive Industry According To Iso/Ts 16949 Requirements DILEK PINAR ÖZTOP 1, ASLI AKSOY 2,*, NURSEL ÖZTÜRK 2 1 HONDA TR Purchasing Department, 41480, Çayırova - Gebze,
Artificial neural networks
Artificial neural networks Now Neurons Neuron models Perceptron learning Multi-layer perceptrons Backpropagation 2 It all starts with a neuron 3 Some facts about human brain ~ 86 billion neurons ~ 10 15
University of Cambridge Engineering Part IIB Module 4F10: Statistical Pattern Processing Handout 8: Multi-Layer Perceptrons
University of Cambridge Engineering Part IIB Module 4F0: Statistical Pattern Processing Handout 8: Multi-Layer Perceptrons x y (x) Inputs x 2 y (x) 2 Outputs x d First layer Second Output layer layer y
WINMAG Graphics Management System
SECTION 10: page 1 Section 10: by Honeywe WINMAG Graphics Management System Contents What is WINMAG? WINMAG Text and Graphics WINMAG Text Ony Scenarios Fire/Emergency Management of Fauts & Disabement Historic
Budgeting Loans from the Social Fund
Budgeting Loans from the Socia Fund tes sheet Pease read these notes carefuy. They expain the circumstances when a budgeting oan can be paid. Budgeting Loans You may be abe to get a Budgeting Loan if:
Multi-Robot Task Scheduling
Proc of IEEE Internationa Conference on Robotics and Automation, Karsruhe, Germany, 013 Muti-Robot Tas Scheduing Yu Zhang and Lynne E Parer Abstract The scheduing probem has been studied extensivey in
Art of Java Web Development By Neal Ford 624 pages US$44.95 Manning Publications, 2004 ISBN: 1-932394-06-0
IEEE DISTRIBUTED SYSTEMS ONLINE 1541-4922 2005 Pubished by the IEEE Computer Society Vo. 6, No. 5; May 2005 Editor: Marcin Paprzycki, http://www.cs.okstate.edu/%7emarcin/ Book Reviews: Java Toos and Frameworks
Secure Network Coding with a Cost Criterion
Secure Network Coding with a Cost Criterion Jianong Tan, Murie Médard Laboratory for Information and Decision Systems Massachusetts Institute of Technoogy Cambridge, MA 0239, USA E-mai: {jianong, medard}@mit.edu
3.5 Pendulum period. 2009-02-10 19:40:05 UTC / rev 4d4a39156f1e. g = 4π2 l T 2. g = 4π2 x1 m 4 s 2 = π 2 m s 2. 3.5 Pendulum period 68
68 68 3.5 Penduum period 68 3.5 Penduum period Is it coincidence that g, in units of meters per second squared, is 9.8, very cose to 2 9.87? Their proximity suggests a connection. Indeed, they are connected
Statistical Machine Learning
Statistical Machine Learning UoC Stats 37700, Winter quarter Lecture 4: classical linear and quadratic discriminants. 1 / 25 Linear separation For two classes in R d : simple idea: separate the classes
Interpreting Individual Classifications of Hierarchical Networks
Interpreting Individua Cassifications of Hierarchica Networks Wi Landecker Michae D. Thomure Luís M. A. Bettencourt Meanie Mitche Garrett T. Kenyon SFI WORKING PAPER: 2013-02-007 SFI Working Papers contain
Predict Influencers in the Social Network
Predict Influencers in the Social Network Ruishan Liu, Yang Zhao and Liuyu Zhou Email: rliu2, yzhao2, lyzhou@stanford.edu Department of Electrical Engineering, Stanford University Abstract Given two persons
Large-Scale Optimization of Hierarchical Features for Saliency Prediction in Natural Images
Large-Scae Optimization of Hierarchica Features for Saiency Prediction in Natura Images Eeonora Vig Harvard University vig@fas.harvard.edu Michae Dorr Harvard Medica Schoo michae.dorr@schepens.harvard.edu
Recurrent Neural Networks
Recurrent Neural Networks Neural Computation : Lecture 12 John A. Bullinaria, 2015 1. Recurrent Neural Network Architectures 2. State Space Models and Dynamical Systems 3. Backpropagation Through Time
Precise assessment of partial discharge in underground MV/HV power cables and terminations
QCM-C-PD-Survey Service Partia discharge monitoring for underground power cabes Precise assessment of partia discharge in underground MV/HV power cabes and terminations Highy accurate periodic PD survey
The Use of Cooling-Factor Curves for Coordinating Fuses and Reclosers
he Use of ooing-factor urves for oordinating Fuses and Recosers arey J. ook Senior Member, IEEE S& Eectric ompany hicago, Iinois bstract his paper describes how to precisey coordinate distribution feeder
Australian Bureau of Statistics Management of Business Providers
Purpose Austraian Bureau of Statistics Management of Business Providers 1 The principa objective of the Austraian Bureau of Statistics (ABS) in respect of business providers is to impose the owest oad
SQL. Ilchul Yoon Assistant Professor State University of New York, Korea. on tables. describing schema. CSE 532 Theory of Database Systems
CSE 532 Theory of Database Systems Lecture 03 SQL Ichu Yoon Assistant Professor State University of New York, Korea Adapted from book authors sides SQL Language for describing database schema & operations
An FDD Wideband CDMA MAC Protocol for Wireless Multimedia Networks
An FDD ideband CDMA MAC Protoco for ireess Mutimedia Networks Xudong ang Broadband and ireess Networking (BN) Lab Schoo of Eectrica and Computer Engineering Georgia Institute of Technoogy Atanta, GA 3332
Advanced ColdFusion 4.0 Application Development - 3 - Server Clustering Using Bright Tiger
Advanced CodFusion 4.0 Appication Deveopment - CH 3 - Server Custering Using Bri.. Page 1 of 7 [Figures are not incuded in this sampe chapter] Advanced CodFusion 4.0 Appication Deveopment - 3 - Server
Hamstring strains. What is a hamstring strain? How do hamstring strains occur? what you ll find in this brochure
what you find in this brochure What is a hamstring strain? How do hamstring strains occur? What you shoud do if a hamstring strain occurs. What rehabiitation you shoud do. Exampe of a return to pay strategy.
Design and Analysis of a Hidden Peer-to-peer Backup Market
Design and Anaysis of a Hidden Peer-to-peer Backup Market Sven Seuken, Denis Chares, Max Chickering, Mary Czerwinski Kama Jain, David C. Parkes, Sidd Puri, and Desney Tan December, 2015 Abstract We present
LADDER SAFETY Table of Contents
Tabe of Contents SECTION 1. TRAINING PROGRAM INTRODUCTION..................3 Training Objectives...........................................3 Rationae for Training.........................................3
TCP/IP Gateways and Firewalls
Gateways and Firewas 1 Gateways and Firewas Prof. Jean-Yves Le Boudec Prof. Andrzej Duda ICA, EPFL CH-1015 Ecubens http://cawww.epf.ch Gateways and Firewas Firewas 2 o architecture separates hosts and
500 IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS, VOL. 31, NO. 3, MARCH 2013
500 IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS, VOL. 3, NO. 3, MARCH 203 Cognitive Radio Network Duaity Agorithms for Utiity Maximization Liang Zheng Chee Wei Tan, Senior Member, IEEE Abstract We
A Practical Framework for Privacy-Preserving Data Analytics
A Practica Framework for Privacy-Preserving Data Anaytics ABSTRACT Liyue Fan Integrated Media Systems Center University of Southern Caifornia Los Angees, CA, USA iyuefan@usc.edu The avaiabiity of an increasing
SELECTING THE SUITABLE ERP SYSTEM: A FUZZY AHP APPROACH. Ufuk Cebeci
SELECTING THE SUITABLE ERP SYSTEM: A FUZZY AHP APPROACH Ufuk Cebeci Department of Industria Engineering, Istanbu Technica University, Macka, Istanbu, Turkey - ufuk_cebeci@yahoo.com Abstract An Enterprise
arxiv:1506.05851v1 [cs.ai] 18 Jun 2015
![arxiv:1506.05851v1 [cs.ai] 18 Jun 2015](/thumbs/31/15228636.jpg "arxiv:1506.05851v1 [cs.ai] 18 Jun 2015")
Smart Pacing for Effective Onine Ad Campaign Optimization Jian Xu, Kuang-chih Lee, Wentong Li, Hang Qi, and Quan Lu Yahoo Inc. 7 First Avenue, Sunnyvae, Caifornia 9489 {xuian,kcee,wentong,hangqi,qu}@yahoo-inc.com
A Branch-and-Price Algorithm for Parallel Machine Scheduling with Time Windows and Job Priorities
A Branch-and-Price Agorithm for Parae Machine Scheduing with Time Windows and Job Priorities Jonathan F. Bard, 1 Siwate Rojanasoonthon 2 1 Graduate Program in Operations Research and Industria Engineering,
Optimizing QoS-Aware Semantic Web Service Composition
Optimizing QoS-Aware Semantic Web Service Composition Freddy Lécué The University of Manchester Booth Street East, Manchester, UK {(firstname.astname)@manchester.ac.uk} Abstract. Ranking and optimization
Performance Evaluation of Artificial Neural. Networks for Spatial Data Analysis
Contemporary Engineering Sciences, Vol. 4, 2011, no. 4, 149-163 Performance Evaluation of Artificial Neural Networks for Spatial Data Analysis Akram A. Moustafa Department of Computer Science Al al-bayt
Machine Learning and Data Mining -
Machine Learning and Data Mining - Perceptron Neural Networks Nuno Cavalheiro Marques (nmm@di.fct.unl.pt) Spring Semester 2010/2011 MSc in Computer Science Multi Layer Perceptron Neurons and the Perceptron
Artificial Neural Networks and Support Vector Machines. CS 486/686: Introduction to Artificial Intelligence
Artificial Neural Networks and Support Vector Machines CS 486/686: Introduction to Artificial Intelligence 1 Outline What is a Neural Network? - Perceptron learners - Multi-layer networks What is a Support
Spatio-Temporal Asynchronous Co-Occurrence Pattern for Big Climate Data towards Long-Lead Flood Prediction
Spatio-Tempora Asynchronous Co-Occurrence Pattern for Big Cimate Data towards Long-Lead Food Prediction Chung-Hsien Yu, Dong Luo, Wei Ding, Joseph Cohen, David Sma and Shafiqu Isam Department of Computer
SMORN-VII REPORT NEURAL NETWORK BENCHMARK ANALYSIS RESULTS & FOLLOW-UP 96. Özer CIFTCIOGLU Istanbul Technical University, ITU. and
NEA/NSC-DOC (96)29 AUGUST 1996 SMORN-VII REPORT NEURAL NETWORK BENCHMARK ANALYSIS RESULTS & FOLLOW-UP 96 Özer CIFTCIOGLU Istanbul Technical University, ITU and Erdinç TÜRKCAN Netherlands Energy Research
IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS, VOL. 31, NO. 12, DECEMBER 2013 1
IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS, VOL. 31, NO. 12, DECEMBER 2013 1 Scaabe Muti-Cass Traffic Management in Data Center Backbone Networks Amitabha Ghosh, Sangtae Ha, Edward Crabbe, and Jennifer
On Capacity Scaling in Arbitrary Wireless Networks
On Capacity Scaing in Arbitrary Wireess Networks Urs Niesen, Piyush Gupta, and Devavrat Shah 1 Abstract arxiv:07112745v3 [csit] 3 Aug 2009 In recent work, Özgür, Lévêque, and Tse 2007) obtained a compete
PATTERN RECOGNITION AND MACHINE LEARNING CHAPTER 4: LINEAR MODELS FOR CLASSIFICATION
PATTERN RECOGNITION AND MACHINE LEARNING CHAPTER 4: LINEAR MODELS FOR CLASSIFICATION Introduction In the previous chapter, we explored a class of regression models having particularly simple analytical
Design of Follow-Up Experiments for Improving Model Discrimination and Parameter Estimation
Design of Foow-Up Experiments for Improving Mode Discrimination and Parameter Estimation Szu Hui Ng 1 Stephen E. Chick 2 Nationa University of Singapore, 10 Kent Ridge Crescent, Singapore 119260. Technoogy
NCH Software PlayPad Media Player
NCH Software PayPad Media Payer This user guide has been created for use with PayPad Media Payer Version 2.xx NCH Software Technica Support If you have difficuties using PayPad Media Payer pease read the
Presented at the 107th Convention 1999 September 24-27 New York
Room Simuation for Mutichanne Fim and Music 4993 (B-2) Knud Bank Christensen and Thomas Lund TC Eectronic A/S DK-8240 Risskov, Denmark Presented at the 107th Convention 1999 September 24-27 New York This
α α λ α = = λ λ α ψ = = α α α λ λ ψ α = + β = > θ θ β > β β θ θ θ β θ β γ θ β = γ θ > β > γ θ β γ = θ β = θ β = θ β = β θ = β β θ = = = β β θ = + α α α α α = = λ λ λ λ λ λ λ = λ λ α α α α λ ψ + α =
The Simple Pendulum. by Dr. James E. Parks
by Dr. James E. Parks Department of Physics and Astronomy 401 Niesen Physics Buidin The University of Tennessee Knoxvie, Tennessee 37996-100 Copyriht June, 000 by James Edar Parks* *A rihts are reserved.
Chapter 3: e-business Integration Patterns
Chapter 3: e-business Integration Patterns Page 1 of 9 Chapter 3: e-business Integration Patterns "Consistency is the ast refuge of the unimaginative." Oscar Wide In This Chapter What Are Integration Patterns?
A New Statistical Approach to Network Anomaly Detection
A New Statistica Approach to Network Anomay Detection Christian Caegari, Sandrine Vaton 2, and Michee Pagano Dept of Information Engineering, University of Pisa, ITALY E-mai: {christiancaegari,mpagano}@ietunipiit
Wide-Area Traffic Management for. Cloud Services
Wide-Area Traffic Management for Coud Services Joe Wenjie Jiang A Dissertation Presented to the Facuty of Princeton University in Candidacy for the Degree of Doctor of Phiosophy Recommended for Acceptance
Neural Networks algorithms and applications
Neural Networks algorithms and applications By Fiona Nielsen 4i 12/12-2001 Supervisor: Geert Rasmussen Niels Brock Business College 1 Introduction Neural Networks is a field of Artificial Intelligence
Betting Strategies, Market Selection, and the Wisdom of Crowds
Betting Strategies, Market Seection, and the Wisdom of Crowds Wiemien Kets Northwestern University w-kets@keogg.northwestern.edu David M. Pennock Microsoft Research New York City dpennock@microsoft.com
The Basel II Risk Parameters. Second edition
The Base II Risk Parameters Second edition . Bernd Engemann Editors Robert Rauhmeier The Base II Risk Parameters Estimation, Vaidation, Stress Testing with Appications to Loan Risk Management Editors Dr.
Measuring operational risk in financial institutions
Measuring operationa risk in financia institutions Operationa risk is now seen as a major risk for financia institutions. This paper considers the various methods avaiabe to measure operationa risk, and
CERTIFICATE COURSE ON CLIMATE CHANGE AND SUSTAINABILITY. Course Offered By: Indian Environmental Society
CERTIFICATE COURSE ON CLIMATE CHANGE AND SUSTAINABILITY Course Offered By: Indian Environmenta Society INTRODUCTION The Indian Environmenta Society (IES) a dynamic and fexibe organization with a goba vision
Neural Networks: a replacement for Gaussian Processes?
Neural Networks: a replacement for Gaussian Processes? Matthew Lilley and Marcus Frean Victoria University of Wellington, P.O. Box 600, Wellington, New Zealand marcus@mcs.vuw.ac.nz http://www.mcs.vuw.ac.nz/
ACO and SVM Selection Feature Weighting of Network Intrusion Detection Method
, pp. 129-270 http://dx.doi.org/10.14257/ijsia.2015.9.4.24 ACO and SVM Seection Feature Weighting of Network Intrusion Detection Method Wang Xingzhu Furong Coege Hunan, University of Arts and Science,
Machine Learning and Pattern Recognition Logistic Regression
Machine Learning and Pattern Recognition Logistic Regression Course Lecturer:Amos J Storkey Institute for Adaptive and Neural Computation School of Informatics University of Edinburgh Crichton Street,
Spherical Correlation of Visual Representations for 3D Model Retrieval
Noname manuscript No. (wi be inserted by the editor) Spherica Correation of Visua Representations for 3D Mode Retrieva Ameesh Makadia Kostas Daniiidis the date of receipt and acceptance shoud be inserted
TERM INSURANCE CALCULATION ILLUSTRATED. This is the U.S. Social Security Life Table, based on year 2007.
This is the U.S. Socia Security Life Tabe, based on year 2007. This is avaiabe at http://www.ssa.gov/oact/stats/tabe4c6.htm. The ife eperiences of maes and femaes are different, and we usuay do separate
Telephony Trainers with Discovery Software
Teephony Trainers 58 Series Teephony Trainers with Discovery Software 58-001 Teephony Training System 58-002 Digita Switching System 58-003 Digita Teephony Training System 58-004 Digita Trunk Network System
Link Dimensioning and LSP Optimization for MPLS Networks Supporting DiffServ EF and BE traffic classes
Link Dimensioning and LSP Optimization for MPLS Networks Supporting DiffServ EF and BE traffic casses Kehang Wu and Dougas S. Reeves Departments of Eectrica and Computer Engineering and Computer Science
Cooperative Content Distribution and Traffic Engineering in an ISP Network
Cooperative Content Distribution and Traffic Engineering in an ISP Network Wenjie Jiang, Rui Zhang-Shen, Jennifer Rexford, Mung Chiang Department of Computer Science, and Department of Eectrica Engineering
Business schools are the academic setting where. The current crisis has highlighted the need to redefine the role of senior managers in organizations.
c r o s os r oi a d s REDISCOVERING THE ROLE OF BUSINESS SCHOOLS The current crisis has highighted the need to redefine the roe of senior managers in organizations. JORDI CANALS Professor and Dean, IESE
Online Supplement for The Robust Network Loading Problem under Hose Demand Uncertainty: Formulation, Polyhedral Analysis, and Computations
Onine Suppement for The Robust Network Loading Probem under Hose Demand Uncertaint: Formuation, Pohedra Anasis, and Computations Aşegü Atın Department of Industria Engineering, TOBB Universit of Economics
Teamwork. Abstract. 2.1 Overview
2 Teamwork Abstract This chapter presents one of the basic eements of software projects teamwork. It addresses how to buid teams in a way that promotes team members accountabiity and responsibiity, and
These slides follow closely the (English) course textbook Pattern Recognition and Machine Learning by Christopher Bishop
Music and Machine Learning (IFT6080 Winter 08) Prof. Douglas Eck, Université de Montréal These slides follow closely the (English) course textbook Pattern Recognition and Machine Learning by Christopher
APPENDIX 10.1: SUBSTANTIVE AUDIT PROGRAMME FOR PRODUCTION WAGES: TROSTON PLC
Appendix 10.1: substantive audit programme for production wages: Troston pc 389 APPENDIX 10.1: SUBSTANTIVE AUDIT PROGRAMME FOR PRODUCTION WAGES: TROSTON PLC The detaied audit programme production wages