Author: Aurelien Bellet,Amaury Habrard,Marc Sebban
Publisher: Morgan & Claypool Publishers
Similarity between objects plays an important role in both human cognitive processes and artificial systems for recognition and categorization. How to appropriately measure such similarities for a given task is crucial to the performance of many machine learning, pattern recognition and data mining methods. This book is devoted to metric learning, a set of techniques to automatically learn similarity and distance functions from data that has attracted a lot of interest in machine learning and related fields in the past ten years. In this book, we provide a thorough review of the metric learning literature that covers algorithms, theory and applications for both numerical and structured data. We first introduce relevant definitions and classic metric functions, as well as examples of their use in machine learning and data mining. We then review a wide range of metric learning algorithms, starting with the simple setting of linear distance and similarity learning. We show how one may scale-up these methods to very large amounts of training data. To go beyond the linear case, we discuss methods that learn nonlinear metrics or multiple linear metrics throughout the feature space, and review methods for more complex settings such as multi-task and semi-supervised learning. Although most of the existing work has focused on numerical data, we cover the literature on metric learning for structured data like strings, trees, graphs and time series. In the more technical part of the book, we present some recent statistical frameworks for analyzing the generalization performance in metric learning and derive results for some of the algorithms presented earlier. Finally, we illustrate the relevance of metric learning in real-world problems through a series of successful applications to computer vision, bioinformatics and information retrieval.
Author: Brian Kulis
Publisher: Now Pub
Metric Learning: A Review presents an overview of existing research in metric learning, including recent progress on scaling to high-dimensional feature spaces and to data sets with an extremely large number of data points. It presents as unified a framework as possible under which existing research on metric learning can be cast.
Author: David Zhang,Yong Xu,Wangmeng Zuo
This monograph describes the latest advances in discriminative learning methods for biometric recognition. Specifically, it focuses on three representative categories of methods: sparse representation-based classification, metric learning, and discriminative feature representation, together with their applications in palmprint authentication, face recognition and multi-biometrics. The ideas, algorithms, experimental evaluation and underlying rationales are also provided for a better understanding of these methods. Lastly, it discusses several promising research directions in the field of discriminative biometric recognition.
Theory, Algorithms and Applications
Author: Hong Cheng
This unique text/reference presents a comprehensive review of the state of the art in sparse representations, modeling and learning. The book examines both the theoretical foundations and details of algorithm implementation, highlighting the practical application of compressed sensing research in visual recognition and computer vision. Topics and features: describes sparse recovery approaches, robust and efficient sparse representation, and large-scale visual recognition; covers feature representation and learning, sparsity induced similarity, and sparse representation and learning-based classifiers; discusses low-rank matrix approximation, graphical models in compressed sensing, collaborative representation-based classification, and high-dimensional nonlinear learning; includes appendices outlining additional computer programming resources, and explaining the essential mathematics required to understand the book.
European Conference, ECML PKDD 2012, Bristol, UK, September 24-28, 2012. Proceedings
Author: Peter A. Flach,Tijl De Bie,Nello Cristianini
This two-volume set LNAI 7523 and LNAI 7524 constitutes the refereed proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases: ECML PKDD 2012, held in Bristol, UK, in September 2012. The 105 revised research papers presented together with 5 invited talks were carefully reviewed and selected from 443 submissions. The final sections of the proceedings are devoted to Demo and Nectar papers. The Demo track includes 10 papers (from 19 submissions) and the Nectar track includes 4 papers (from 14 submissions). The papers grouped in topical sections on association rules and frequent patterns; Bayesian learning and graphical models; classification; dimensionality reduction, feature selection and extraction; distance-based methods and kernels; ensemble methods; graph and tree mining; large-scale, distributed and parallel mining and learning; multi-relational mining and learning; multi-task learning; natural language processing; online learning and data streams; privacy and security; rankings and recommendations; reinforcement learning and planning; rule mining and subgroup discovery; semi-supervised and transductive learning; sensor data; sequence and string mining; social network mining; spatial and geographical data mining; statistical methods and evaluation; time series and temporal data mining; and transfer learning.
European Conference, ECML PKDD 2015, Porto, Portugal, September 7-11, 2015, Proceedings
Author: Annalisa Appice,Pedro Pereira Rodrigues,Vítor Santos Costa,Carlos Soares,João Gama,Alípio Jorge
The three volume set LNAI 9284, 9285, and 9286 constitutes the refereed proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2015, held in Porto, Portugal, in September 2015. The 131 papers presented in these proceedings were carefully reviewed and selected from a total of 483 submissions. These include 89 research papers, 11 industrial papers, 14 nectar papers, and 17 demo papers. They were organized in topical sections named: classification, regression and supervised learning; clustering and unsupervised learning; data preprocessing; data streams and online learning; deep learning; distance and metric learning; large scale learning and big data; matrix and tensor analysis; pattern and sequence mining; preference learning and label ranking; probabilistic, statistical, and graphical approaches; rich data; and social and graphs. Part III is structured in industrial track, nectar track, and demo track.
The Comprehensive Resource for Learning and Teaching the Metric System (SI)
Author: Dennis R. Brownridge
Publisher: Professional Publications Incorporated
Metric in Minutes is the perfect resource for comprehensive, up-to-date information on the International System of Units (SI), the official metric system adopted by the United States and most of the world. Written by a teacher and designed for self-instruction or use in high school, college, or continuing education classes, Metric in Minutes explains the metric system in clear, accessible language. Author Dennis Brownridge, who has been teaching the metric system for years, covers everything you need to know about SI, from its history to practical tips on conversions and problem solving. Metric in Minutes ... clarifies the logic and structure of the metric system; defines the basic metric units and the quantities of nature they measure; shows how to use metric prefixes, with easy-to-remember tips; features over 400 conversion factors for nonmetric units; gives rules for correct spelling, capitalization, pronunciation, and mathematical use of metric symbols and units; and includes practice problems and answers, great for self-testing or classroom use. Whether you're learning the metric system for the first time, need a refresher for work or school, or require an authoritative reference for everyday use, you can rely on Metric in Minutes.
Algorithms and Applications
Author: Charu C. Aggarwal
Publisher: CRC Press
Category: Business & Economics
Comprehensive Coverage of the Entire Area of Classification Research on the problem of classification tends to be fragmented across such areas as pattern recognition, database, data mining, and machine learning. Addressing the work of these different communities in a unified way, Data Classification: Algorithms and Applications explores the underlying algorithms of classification as well as applications of classification in a variety of problem domains, including text, multimedia, social network, and biological data. This comprehensive book focuses on three primary aspects of data classification: Methods: The book first describes common techniques used for classification, including probabilistic methods, decision trees, rule-based methods, instance-based methods, support vector machine methods, and neural networks. Domains: The book then examines specific methods used for data domains such as multimedia, text, time-series, network, discrete sequence, and uncertain data. It also covers large data sets and data streams due to the recent importance of the big data paradigm. Variations: The book concludes with insight on variations of the classification process. It discusses ensembles, rare-class learning, distance function learning, active learning, visual learning, transfer learning, and semi-supervised learning as well as evaluation aspects of classifiers.
16th International Conference, ICONIP 2009, Bangkok, Thailand, December 1-5, 2009, Proceedings
Author: Chi-Sing Leung
Publisher: Springer Science & Business Media
The two volumes LNCS 5863 and 5864 constitute the proceedings of the 16th International Conference on Neural Information Processing, ICONIP 2009, held in Bangkok, Thailand, in December 2009. The 145 regular session papers and 53 special session papers presented were carefully reviewed and selected from 466 submissions. The papers are structured in topical sections on cognitive science and computational neuroscience, neurodynamics, mathematical modeling and analysis, kernel and related methods, learning algorithms, pattern analysis, face analysis and processing, image processing, financial applications, computer vision, control and robotics, evolutionary computation, other emerging computational methods, signal, data and text processing, artificial spiking neural systems: nonlinear dynamics and engineering applications, towards brain-inspired systems, computational advances in bioinformatics, data mining for cybersecurity, evolutionary neural networks: theory and practice, hybrid and adaptive systems for computer vision and robot control, intelligent data mining, neural networks for data mining, and SOM and related subjects and its applications.
10th International Conference, ICIC 2014, Taiyuan, China, August 3-6, 2014, Proceedings
Author: De-Shuang Huang,Kyungsook Han,Michael Gromiha
This book – in conjunction with the volumes LNCS 8588 and LNAI 8589 – constitutes the refereed proceedings of the 10th International Conference on Intelligent Computing, ICIC 2014, held in Taiyuan, China, in August 2014. The 58 papers of this volume were carefully reviewed and selected from numerous submissions. The papers are organized in topical sections such as machine learning; neural networks; image processing; computational systems biology and medical informatics; biomedical informatics theory and methods; advances on bio-inspired computing; protein and gene bioinformatics: analysis, algorithms, applications.
First International Workshop, MLMI 2010, Held in Conjunction with MICCAI 2010, Beijing, China, September 20, 2010, Proceedings
Author: Fei Wang,Pingkun Yan,Kenji Suzuki,Dinggang Shen
Publisher: Springer Science & Business Media
This book constitutes the refereed proceedings of the First International Workshop on Machine Learning in Medical Imaging, MLMI 2010, held in conjunction with MICCAI 2010, in Beijing, China, in September 2010. The 23 revised full papers presented were carefully reviewed and selected from 38 submissions. The papers address topics such as machine learning applications to medical images, medical image analysis, multi-modality fusion, image reconstruction for medical imaging, computer-aided detection/diagnosis, medical image retrieval, cellular image analysis, molecular/pathologic image analysis, and dynamic, functional, physiologic, and anatomic imaging.