Researchers from various disciplines such as pattern recognition, statistics, and machine learning have explored the use of ensemble methodology since the late seventies. Thus, they are faced with a wide variety of methods, given the growing interest in the field. This book aims to impose a degree of order upon this diversity by presenting a coherent and unified repository of ensemble methods, theories, trends, challenges and applications. The book describes in detail the classical methods, as well as the extensions and novel approaches developed recently. Along with algorithmic descriptions of each method, it also explains the circumstances in which this method is applicable and the consequences and the trade-offs incurred by using the method.
{"title":"Pattern Classification Using Ensemble Methods","authors":"L. Rokach","doi":"10.1142/7238","DOIUrl":"https://doi.org/10.1142/7238","url":null,"abstract":"Researchers from various disciplines such as pattern recognition, statistics, and machine learning have explored the use of ensemble methodology since the late seventies. Thus, they are faced with a wide variety of methods, given the growing interest in the field. This book aims to impose a degree of order upon this diversity by presenting a coherent and unified repository of ensemble methods, theories, trends, challenges and applications. The book describes in detail the classical methods, as well as the extensions and novel approaches developed recently. Along with algorithmic descriptions of each method, it also explains the circumstances in which this method is applicable and the consequences and the trade-offs incurred by using the method.","PeriodicalId":440867,"journal":{"name":"Series in Machine Perception and Artificial Intelligence","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114017165","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2009-11-01DOI: 10.1142/9789814271073_0002
L. Rokach
{"title":"Introduction to Ensemble Learning","authors":"L. Rokach","doi":"10.1142/9789814271073_0002","DOIUrl":"https://doi.org/10.1142/9789814271073_0002","url":null,"abstract":"","PeriodicalId":440867,"journal":{"name":"Series in Machine Perception and Artificial Intelligence","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124015188","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2009-11-01DOI: 10.1142/9789814271073_0007
L. Rokach
{"title":"Evaluating Ensembles of Classifiers","authors":"L. Rokach","doi":"10.1142/9789814271073_0007","DOIUrl":"https://doi.org/10.1142/9789814271073_0007","url":null,"abstract":"","PeriodicalId":440867,"journal":{"name":"Series in Machine Perception and Artificial Intelligence","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133576719","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Continuous Wavelet Transforms Multiresolution Analysis and Wavelet Bases Some Typical Wavelet Bases Edge Detection by Wavelet Transform Construction of New Wavelet Function and Application to Curve Analysis Feature Extraction by Wavelet Sub-Patterns and Divider Dimension Document Analysis by Reference Line Detection with 2-D Wavelet Transform Chinese Character Processing with B-Spline Wavelet Transform Classifier Design Based on Orthogonal Wavelet Series Skeletonization of Ribbon-like Shapes with New Wavelet Function Face Recognition Based on Non-Tensor Product Wavelets.
{"title":"Wavelet Theory Approach to Pattern Recognition - 2nd Edition","authors":"Y. Tang","doi":"10.1142/7324","DOIUrl":"https://doi.org/10.1142/7324","url":null,"abstract":"Continuous Wavelet Transforms Multiresolution Analysis and Wavelet Bases Some Typical Wavelet Bases Edge Detection by Wavelet Transform Construction of New Wavelet Function and Application to Curve Analysis Feature Extraction by Wavelet Sub-Patterns and Divider Dimension Document Analysis by Reference Line Detection with 2-D Wavelet Transform Chinese Character Processing with B-Spline Wavelet Transform Classifier Design Based on Orthogonal Wavelet Series Skeletonization of Ribbon-like Shapes with New Wavelet Function Face Recognition Based on Non-Tensor Product Wavelets.","PeriodicalId":440867,"journal":{"name":"Series in Machine Perception and Artificial Intelligence","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129626163","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Learning from structured data is becoming increasingly important. However, most prior work on kernel methods has focused on learning from attribute-value data. Only recently have researchers started investigating kernels for structured data. This paper describes how kernel definitions can be simplified by identifying the structure of the data and how kernels can be defined on this structure. We propose a kernel for structured data, prove that it is positive definite, and show how it can be adapted in practical applications.
{"title":"Kernels for structured data","authors":"Thomas Gärtner","doi":"10.1142/6855","DOIUrl":"https://doi.org/10.1142/6855","url":null,"abstract":"Learning from structured data is becoming increasingly important. However, most prior work on kernel methods has focused on learning from attribute-value data. Only recently have researchers started investigating kernels for structured data. This paper describes how kernel definitions can be simplified by identifying the structure of the data and how kernels can be defined on this structure. We propose a kernel for structured data, prove that it is positive definite, and show how it can be adapted in practical applications.","PeriodicalId":440867,"journal":{"name":"Series in Machine Perception and Artificial Intelligence","volume":"68 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121560796","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Classification Methods Linguistic Resources and Handwriting Databases Off-Line Approach On-Line Approach Multiple Classifier Combination Writer-Dependent Recognition.
分类方法语言资源与手写数据库离线方法在线方法多分类器组合写作者依赖识别。
{"title":"Recognition of Whiteboard Notes - Online, Offline and Combination","authors":"M. Liwicki, H. Bunke","doi":"10.1142/6854","DOIUrl":"https://doi.org/10.1142/6854","url":null,"abstract":"Classification Methods Linguistic Resources and Handwriting Databases Off-Line Approach On-Line Approach Multiple Classifier Combination Writer-Dependent Recognition.","PeriodicalId":440867,"journal":{"name":"Series in Machine Perception and Artificial Intelligence","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115028789","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Decision trees have become one of the most powerful and popular approaches in knowledge discovery and data mining; it is the science of exploring large and complex bodies of data in order to discover useful patterns. Decision tree learning continues to evolve over time. Existing methods are constantly being improved and new methods introduced. This 2nd Edition is dedicated entirely to the field of decision trees in data mining; to cover all aspects of this important technique, as well as improved or new methods and techniques developed after the publication of our first edition. In this new edition, all chapters have been revised and new topics brought in. New topics include Cost-Sensitive Active Learning, Learning with Uncertain and Imbalanced Data, Using Decision Trees beyond Classification Tasks, Privacy Preserving Decision Tree Learning, Lessons Learned from Comparative Studies, and Learning Decision Trees for Big Data. A walk-through guide to existing open-source data mining software is also included in this edition. This book invites readers to explore the many benefits in data mining that decision trees offer: Self-explanatory and easy to follow when compacted Able to handle a variety of input data: nominal, numeric and textual Scales well to big data Able to process datasets that may have errors or missing values High predictive performance for a relatively small computational effort Available in many open source data mining packages over a variety of platforms Useful for various tasks, such as classification, regression, clustering and feature selection Readership: Researchers, graduate and undergraduate students in information systems, engineering, computer science, statistics and management.
{"title":"Data Mining with Decision Trees - Theory and Applications","authors":"L. Rokach, O. Maimon","doi":"10.1142/6604","DOIUrl":"https://doi.org/10.1142/6604","url":null,"abstract":"Decision trees have become one of the most powerful and popular approaches in knowledge discovery and data mining; it is the science of exploring large and complex bodies of data in order to discover useful patterns. Decision tree learning continues to evolve over time. Existing methods are constantly being improved and new methods introduced. This 2nd Edition is dedicated entirely to the field of decision trees in data mining; to cover all aspects of this important technique, as well as improved or new methods and techniques developed after the publication of our first edition. In this new edition, all chapters have been revised and new topics brought in. New topics include Cost-Sensitive Active Learning, Learning with Uncertain and Imbalanced Data, Using Decision Trees beyond Classification Tasks, Privacy Preserving Decision Tree Learning, Lessons Learned from Comparative Studies, and Learning Decision Trees for Big Data. A walk-through guide to existing open-source data mining software is also included in this edition. This book invites readers to explore the many benefits in data mining that decision trees offer: Self-explanatory and easy to follow when compacted Able to handle a variety of input data: nominal, numeric and textual Scales well to big data Able to process datasets that may have errors or missing values High predictive performance for a relatively small computational effort Available in many open source data mining packages over a variety of platforms Useful for various tasks, such as classification, regression, clustering and feature selection Readership: Researchers, graduate and undergraduate students in information systems, engineering, computer science, statistics and management.","PeriodicalId":440867,"journal":{"name":"Series in Machine Perception and Artificial Intelligence","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116770362","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In graph-based structural pattern recognition, the idea is to transform patterns into graphs and perform the analysis and recognition of patterns in the graph domain - commonly referred to as graph matching. A large number of methods for graph matching have been proposed. Graph edit distance, for instance, defines the dissimilarity of two graphs by the amount of distortion that is needed to transform one graph into the other and is considered one of the most flexible methods for error-tolerant graph matching.This book focuses on graph kernel functions that are highly tolerant towards structural errors. The basic idea is to incorporate concepts from graph edit distance into kernel functions, thus combining the flexibility of edit distance-based graph matching with the power of kernel machines for pattern recognition. The authors introduce a collection of novel graph kernels related to edit distance, including diffusion kernels, convolution kernels, and random walk kernels. From an experimental evaluation of a semi-artificial line drawing data set and four real-world data sets consisting of pictures, microscopic images, fingerprints, and molecules, the authors demonstrate that some of the kernel functions in conjunction with support vector machines significantly outperform traditional edit distance-based nearest-neighbor classifiers, both in terms of classification accuracy and running time.
{"title":"Bridging the Gap between Graph Edit Distance and Kernel Machines","authors":"M. Neuhaus, H. Bunke","doi":"10.1142/6523","DOIUrl":"https://doi.org/10.1142/6523","url":null,"abstract":"In graph-based structural pattern recognition, the idea is to transform patterns into graphs and perform the analysis and recognition of patterns in the graph domain - commonly referred to as graph matching. A large number of methods for graph matching have been proposed. Graph edit distance, for instance, defines the dissimilarity of two graphs by the amount of distortion that is needed to transform one graph into the other and is considered one of the most flexible methods for error-tolerant graph matching.This book focuses on graph kernel functions that are highly tolerant towards structural errors. The basic idea is to incorporate concepts from graph edit distance into kernel functions, thus combining the flexibility of edit distance-based graph matching with the power of kernel machines for pattern recognition. The authors introduce a collection of novel graph kernels related to edit distance, including diffusion kernels, convolution kernels, and random walk kernels. From an experimental evaluation of a semi-artificial line drawing data set and four real-world data sets consisting of pictures, microscopic images, fingerprints, and molecules, the authors demonstrate that some of the kernel functions in conjunction with support vector machines significantly outperform traditional edit distance-based nearest-neighbor classifiers, both in terms of classification accuracy and running time.","PeriodicalId":440867,"journal":{"name":"Series in Machine Perception and Artificial Intelligence","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128419109","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2006-06-20DOI: 10.1007/S11432-006-0339-9
L. Hongxing
{"title":"Probability Representations of Fuzzy Systems","authors":"L. Hongxing","doi":"10.1007/S11432-006-0339-9","DOIUrl":"https://doi.org/10.1007/S11432-006-0339-9","url":null,"abstract":"","PeriodicalId":440867,"journal":{"name":"Series in Machine Perception and Artificial Intelligence","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121689270","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"The Dissimilarity Representation for Pattern Recognition - Foundations and Applications","authors":"E. Pekalska, R. Duin","doi":"10.1142/5965","DOIUrl":"https://doi.org/10.1142/5965","url":null,"abstract":"# Spaces # Characterization of Dissimilarities # Learning Approaches # Dissimilarity Measures # Visualization # Further Data Exploration # One-Class Classifiers # Classification # Combining # Representation Review and Recommendations # Conclusions and Open Problems","PeriodicalId":440867,"journal":{"name":"Series in Machine Perception and Artificial Intelligence","volume":"174 ","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2005-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133287512","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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