Semi-Supervised and Unsupervised Machine Learning. Novel Strategies

Transcription

1 Brochure More information from Semi-Supervised and Unsupervised Machine Learning. Novel Strategies Description: This book provides a detailed and up to date overview on classification and data mining methods. The first part is focused on supervised classification algorithms and their applications, including recent research on the combination of classifiers. The second part deals with unsupervised data mining and knowledge discovery, with special attention to text mining. Discovering the underlying structure on a data set has been a key research topic associated to unsupervised techniques with multiple applications and challenges, from web content mining to the inference of cancer subtypes in genomic microarray data. Among those, the book focuses on a new application for dialog systems which can be thereby made adaptable and portable to different domains. Clustering evaluation metrics and new approaches, such as the ensembles of clustering algorithms, are also described. Contents: PART 1. STATE OF THE ART 1 Chapter 1. Introduction Organization of the book Utterance corpus Datasets from the UCI repository Microarray dataset Simulated datasets 14 Chapter 2. State of the Art in Clustering and Semi Supervised Techniques Introduction Unsupervised machine learning (clustering) A brief history of cluster analysis Cluster algorithms Applications of cluster analysis Evaluation methods Internal cluster evaluation External cluster validation Semi supervised learning Summary 88 PART 2. APPROACHES TO SEMI SUPERVISED CLASSIFICATION 91 Chapter 3. Semi Supervised Classification Using Prior Word Clustering Introduction Dataset 94

2 3.3. Utterance classification scheme Semi supervised approach based on term clustering Disambiguation Summary 124 Chapter 4. Semi Supervised Classification Using Pattern Clustering Introduction New semi supervised algorithm using the cluster and label strategy Optimum cluster labeling Supervised classification block Datasets An analysis of the bounds for the cluster and label approaches Extension through cluster pruning Simulations and results Summary 179 PART 3. CONTRIBUTIONS TO UNSUPERVISED CLASSIFICATION NUMBER OF CLUSTERS 183 ALGORITHMS TO DETECT THE OPTIMAL Chapter 5. Detection of the Number of Clusters through Non Parametric Clustering Algorithms Introduction New hierarchical pole based clustering algorithm Evaluation Datasets Summary 197 Chapter 6. Detecting the Number of Clusters through Cluster Validation Introduction Cluster validation methods Combination approach based on quantiles Datasets Results Application of speech utterances Summary 224 Bibliography 227 Index 243

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