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Pattern Recognition and Machine Learning Product Detail:

  • Publisher : Springer Verlag
  • Release : 17 August 2006
  • ISBN : 0387310738
  • Page : 738 pages
  • Rating : 3/5 from 1 voters

Pattern Recognition and Machine Learning Book Summary/Review:

This is the first text on pattern recognition to present the Bayesian viewpoint, one that has become increasing popular in the last five years. It presents approximate inference algorithms that permit fast approximate answers in situations where exact answers are not feasible. It provides the first text to use graphical models to describe probability distributions when there are no other books that apply graphical models to machine learning. It is also the first four-color book on pattern recognition. The book is suitable for courses on machine learning, statistics, computer science, signal processing, computer vision, data mining, and bioinformatics. Extensive support is provided for course instructors, including more than 400 exercises, graded according to difficulty. Example solutions for a subset of the exercises are available from the book web site, while solutions for the remainder can be obtained by instructors from the publisher.

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Pattern Recognition and Machine Learning

Pattern Recognition and Machine Learning
  • Author : Christopher M. Bishop
  • Publisher : Springer Verlag
  • Release Date : 2006-08-17
  • ISBN : 0387310738
GET THIS BOOKPattern Recognition and Machine Learning

This is the first text on pattern recognition to present the Bayesian viewpoint, one that has become increasing popular in the last five years. It presents approximate inference algorithms that permit fast approximate answers in situations where exact answers are not feasible. It provides the first text to use graphical models to describe probability distributions when there are no other books that apply graphical models to machine learning. It is also the first four-color book on pattern recognition. The book

Pattern Recognition and Machine Learning

Pattern Recognition and Machine Learning
  • Author : Y. Anzai
  • Publisher : Elsevier
  • Release Date : 2012-12-02
  • ISBN : 9780080513638
GET THIS BOOKPattern Recognition and Machine Learning

This is the first text to provide a unified and self-contained introduction to visual pattern recognition and machine learning. It is useful as a general introduction to artifical intelligence and knowledge engineering, and no previous knowledge of pattern recognition or machine learning is necessary. Basic for various pattern recognition and machine learning methods. Translated from Japanese, the book also features chapter exercises, keywords, and summaries.

Pattern Recognition and Machine Learning

Pattern Recognition and Machine Learning
  • Author : Christopher M. Bishop
  • Publisher : Unknown
  • Release Date : 2013
  • ISBN : 8132209060
GET THIS BOOKPattern Recognition and Machine Learning

The field of pattern recognition has undergone substantial development over the years. This book reflects these developments while providing a grounding in the basic concepts of pattern recognition and machine learning. It is aimed at advanced undergraduates or first year PhD students, as well as researchers and practitioners.

Fundamentals of Pattern Recognition and Machine Learning

Fundamentals of Pattern Recognition and Machine Learning
  • Author : Ulisses Braga-Neto
  • Publisher : Springer Nature
  • Release Date : 2020-09-10
  • ISBN : 9783030276560
GET THIS BOOKFundamentals of Pattern Recognition and Machine Learning

Fundamentals of Pattern Recognition and Machine Learning is designed for a one or two-semester introductory course in Pattern Recognition or Machine Learning at the graduate or advanced undergraduate level. The book combines theory and practice and is suitable to the classroom and self-study. It has grown out of lecture notes and assignments that the author has developed while teaching classes on this topic for the past 13 years at Texas A&M University. The book is intended to be concise but

Machine Learning and Data Mining in Pattern Recognition

Machine Learning and Data Mining in Pattern Recognition
  • Author : Petra Perner
  • Publisher : Springer Science & Business Media
  • Release Date : 2009-07-21
  • ISBN : 9783642030703
GET THIS BOOKMachine Learning and Data Mining in Pattern Recognition

There is no royal road to science, and only those who do not dread the fatiguing climb of its steep paths have a chance of gaining its luminous summits. Karl Marx A Universial Genius of the 19th Century Many scientists from all over the world during the past two years since the MLDM 2007 have come along on the stony way to the sunny summit of science and have worked hard on new ideas and applications in the area of data

Introduction to Pattern Recognition and Machine Learning

Introduction to Pattern Recognition and Machine Learning
  • Author : M Narasimha Murty,V Susheela Devi
  • Publisher : World Scientific
  • Release Date : 2015-04-22
  • ISBN : 9789814656276
GET THIS BOOKIntroduction to Pattern Recognition and Machine Learning

This book adopts a detailed and methodological algorithmic approach to explain the concepts of pattern recognition. While the text provides a systematic account of its major topics such as pattern representation and nearest neighbour based classifiers, current topics — neural networks, support vector machines and decision trees — attributed to the recent vast progress in this field are also dealt with. Introduction to Pattern Recognition and Machine Learning will equip readers, especially senior computer science undergraduates, with a deeper understanding of the

Pattern Recognition and Neural Networks

Pattern Recognition and Neural Networks
  • Author : Brian D. Ripley
  • Publisher : Cambridge University Press
  • Release Date : 2007
  • ISBN : 0521717701
GET THIS BOOKPattern Recognition and Neural Networks

This 1996 book explains the statistical framework for pattern recognition and machine learning, now in paperback.

Pattern Recognition and Machine Intelligence

Pattern Recognition and Machine Intelligence
  • Author : Rajat K. De
  • Publisher : Springer Science & Business Media
  • Release Date : 2007-11-29
  • ISBN : 9783540770459
GET THIS BOOKPattern Recognition and Machine Intelligence

This book constitutes the refereed proceedings of the Second International Conference on Pattern Recognition and Machine Intelligence, PReMI 2007, held in Kolkata, India in December 2007. The 82 revised papers presented were carefully reviewed and selected from 241 submissions. The papers are organized in topical sections on pattern recognition, image analysis, soft computing and applications, data mining and knowledge discovery, bioinformatics, signal and speech processing, document analysis and text mining, biometrics, and video analysis.

Pattern Recognition and Machine Learning by Christopher M. Bishop

Pattern Recognition and Machine Learning by Christopher M. Bishop
  • Author : Christopher M. Bishop
  • Publisher : Unknown
  • Release Date : 2006
  • ISBN : OCLC:1109603725
GET THIS BOOKPattern Recognition and Machine Learning by Christopher M. Bishop

Pattern Recognition and Machine Intelligence

Pattern Recognition and Machine Intelligence
  • Author : Sergei O. Kuznetsov,Deba P. Mandal,Malay K. Kundu,Sankar Kumar Pal
  • Publisher : Springer
  • Release Date : 2011-06-25
  • ISBN : 9783642217869
GET THIS BOOKPattern Recognition and Machine Intelligence

This book constitutes the refereed proceedings of the 4th International Conference on Pattern Recognition and Machine Intelligence, PReMI 2011, held in Moscow, Russia in June/July 2011. The 65 revised papers presented together with 5 invited talks were carefully reviewed and selected from 140 submissions. The papers are organized in topical sections on pattern recognition and machine learning; image analysis; image and video information retrieval; natural language processing and text and data mining; watermarking, steganography and biometrics; soft computing and applications; clustering and network analysis;

Machine Learning in Image Analysis and Pattern Recognition

Machine Learning in Image Analysis and Pattern Recognition
  • Author : Munish Kumar , R. K. Sharma,Ishwar Sethi
  • Publisher : MDPI
  • Release Date : 2021-09-08
  • ISBN : 9783036517148
GET THIS BOOKMachine Learning in Image Analysis and Pattern Recognition

This book is to chart the progress in applying machine learning, including deep learning, to a broad range of image analysis and pattern recognition problems and applications. In this book, we have assembled original research articles making unique contributions to the theory, methodology and applications of machine learning in image analysis and pattern recognition.

Machine Learning and Data Mining in Pattern Recognition

Machine Learning and Data Mining in Pattern Recognition
  • Author : Petra Perner,Azriel Rosenfeld
  • Publisher : Springer Science & Business Media
  • Release Date : 2003-06-25
  • ISBN : 9783540405047
GET THIS BOOKMachine Learning and Data Mining in Pattern Recognition

TheInternationalConferenceonMachineLearningandDataMining(MLDM)is the third meeting in a series of biennial events, which started in 1999, organized by the Institute of Computer Vision and Applied Computer Sciences (IBaI) in Leipzig. MLDM began as a workshop and is now a conference, and has brought the topic of machine learning and data mining to the attention of the research community. Seventy-?ve papers were submitted to the conference this year. The program committeeworkedhardtoselectthemostprogressiveresearchinafairandc- petent review process which led to the acceptance of 33 papers

Pattern Recognition and Machine Intelligence

Pattern Recognition and Machine Intelligence
  • Author : Bhabesh Deka,Pradipta Maji,Sushmita Mitra,Dhruba Kumar Bhattacharyya,Prabin Kumar Bora,Sankar Kumar Pal
  • Publisher : Springer Nature
  • Release Date : 2019-11-25
  • ISBN : 9783030348724
GET THIS BOOKPattern Recognition and Machine Intelligence

The two-volume set of LNCS 11941 and 11942 constitutes the refereed proceedings of the 8th International Conference on Pattern Recognition and Machine Intelligence, PReMI 2019, held in Tezpur, India, in December 2019. The 131 revised full papers presented were carefully reviewed and selected from 341 submissions. They are organized in topical sections named: Pattern Recognition; Machine Learning; Deep Learning; Soft and Evolutionary Computing; Image Processing; Medical Image Processing; Bioinformatics and Biomedical Signal Processing; Information Retrieval; Remote Sensing; Signal and Video Processing; and Smart and Intelligent Sensors.

Pattern Recognition and Classification

Pattern Recognition and Classification
  • Author : Geoff Dougherty
  • Publisher : Springer Science & Business Media
  • Release Date : 2012-10-28
  • ISBN : 9781461453239
GET THIS BOOKPattern Recognition and Classification

The use of pattern recognition and classification is fundamental to many of the automated electronic systems in use today. However, despite the existence of a number of notable books in the field, the subject remains very challenging, especially for the beginner. Pattern Recognition and Classification presents a comprehensive introduction to the core concepts involved in automated pattern recognition. It is designed to be accessible to newcomers from varied backgrounds, but it will also be useful to researchers and professionals in

Machine Learning and Data Mining in Pattern Recognition

Machine Learning and Data Mining in Pattern Recognition
  • Author : Petra Perner
  • Publisher : Springer
  • Release Date : 2003-05-15
  • ISBN : 9783540445968
GET THIS BOOKMachine Learning and Data Mining in Pattern Recognition

This book constitutes the refereed proceedings of the Second International Workshop on Machine Learning and Data Mining in Pattern Recognition, MLDM 2001, held in Leipzig, Germany in July 2001. The 26 revised full papers presented together with two invited papers were carefully reviewed and selected for inclusion in the proceedings. The papers are organized in topical sections on case-based reasoning and associative memory; rule induction and grammars; clustering and conceptual clustering; data mining on signals, images, and spatio-temporal data; nonlinear function learning and