Support Vector Machines for Pattern Classification - Springer - Image 1

£112.79

This is the most expensive it has ever been. Walk away.

£113 today · previous high £113 · all-time low £109

Springer Support Vector Machines for Pattern Classification - Springer

Name: Support Vector Machines for Pattern Classification - Springer
Brand: Springer
Price: 112.79 GBP
Availability: InStock

114 black & white illustrations, 114 col

AI & Machine Learning

Price Distribution

Price distribution over 76 days • 2 price levels

Days at Price

Current Price

Price Analysis

Most common price: £109 (68 days, 89.5%)

Price range: £109 - £113

Price levels: 2 different prices over 76 days

Description

This comprehensive guide explores the theory and application of support vector machines in pattern classification tasks. The book provides rigorous performance comparisons between classifiers and regressors, making it essential for understanding SVM capabilities and limitations. It covers multiclass classification architectures, function approximation methods, and detailed evaluation criteria for assessing classifier and regressor performance. The text clarifies two-class SVM fundamentals whilst discussing kernel methods that enhance the generalization ability of neural networks and fuzzy systems. Throughout the book, ample illustrations and practical examples demonstrate real-world applications, with performance evaluation conducted using publicly available datasets. The material examines advanced topics including Mahalanobis kernels and empirical techniques, making it suitable for researchers, practitioners, and students seeking in-depth knowledge of SVMs in computer vision and pattern recognition.

Key Features

Support Vector Machines for Pattern Classification presents rigorous performance comparisons of multiple classifiers and regressors for benchmarking purposes.

The book clarifies characteristics of two-class SVMs and explains kernel methods for improving neural network and fuzzy system generalization.

Multiclass classification architectures and function approximation solutions are detailed with comprehensive evaluation criteria for both classifiers and regressors.

Ample illustrations, practical examples, and publicly available datasets provide hands-on performance evaluation and real-world application demonstrations.

Advanced topics including Mahalanobis kernels and empirical techniques are examined for researchers working in computer vision and pattern recognition.

Product Specifications

Brand: Springer
Model: 114 black & white illustrations, 114 col
Format: hardcover
ASIN: 1849960976
Category: Books > Subjects > Computing & Internet > Computer Science > AI & Machine Learning
Domain: Amazon UK
Release Date: 29 March 2010
Listed Since: 11 January 2010

Barcode

No barcode data available

This is the most expensive it has ever been. Walk away.

Springer Support Vector Machines for Pattern Classification - Springer

Price Distribution

Price Analysis

Description

Key Features

Product Specifications

Barcode

Similar Products You Might Like

Support Vector Machines: Optimization Based Theory, Algorithms, and Extensions (Chapman & Hall/CRC Data Mining and Knowledge Discovery Series)

Support Vector Machines Applications

An Introduction to Support Vector Machines and Other Kernel-based Learning Methods

Machine Learning: A Practical Approach on the Statistical Learning Theory

Support Vector Machines Applications - Springer AI Book

Rule Extraction from Support Vector Machines: 80 (Studies in Computational Intelligence, 80)

Rule Extraction from Support Vector Machines - Studies 80

Regularization, Optimization, Kernels, and Support Vector Machines (Chapman & Hall/CRC Machine Learning & Pattern Recognition)

RBPK & EuDiC : A Narrative on SVM Efficiency Improvement

Regularization, Optimization, Kernels, and Support Vector Machines (Chapman & Hall/CRC Machine Learning & Pattern Recognition)

The Nature of Statistical Learning Theory (Information Science and Statistics)

Enhancing Kernel Methods for Pattern Classification: Theories and Implementations

Support Vector Machines in Chemistry and Biotechnology - CRC Press

MIT Press Learning with Kernels - Support Vector Machines Book

Digital Signal Processing with Kernel Methods (IEEE Press)

Machine Learning with Python Cookbook: Practical Solutions from Preprocessing to Deep Learning

Pattern Recognition Algorithms for Data Mining - AI & ML

Pearson Machine Learning Foundations Volume 1: Supervised Learning

Supervised and Unsupervised Pattern Recognition: Feature Extraction and Computational Intelligence (Industrial Electronics)

Learning with Fractional Orthogonal Kernel Classifiers in Support Vector Machines: Theory, Algorithms and Applications (Industrial and Applied Mathematics)

MIT Press Foundations of Machine Learning Textbook

Machine Learning Algorithms: Popular algorithms for data science and machine learning, 2nd Edition

Machine Learning from Weak Supervision: An Empirical Risk Minimization Approach (Adaptive Computation and Machine Learning series)

Computational Learning Theory: 4th European Conference, EuroCOLT'99 Nordkirchen, Germany, March 29-31, 1999 Proceedings: 1572 (Lecture Notes in Computer Science, 1572)

This is the most expensive it has ever been. Walk away.

Springer Support Vector Machines for Pattern Classification - Springer

Price History & Forecast

Price Distribution

Price Analysis

Description

Key Features

Product Specifications

Barcode

Similar Products You Might Like

Support Vector Machines: Optimization Based Theory, Algorithms, and Extensions (Chapman & Hall/CRC Data Mining and Knowledge Discovery Series)

Support Vector Machines Applications

An Introduction to Support Vector Machines and Other Kernel-based Learning Methods

Machine Learning: A Practical Approach on the Statistical Learning Theory

Support Vector Machines Applications - Springer AI Book

Rule Extraction from Support Vector Machines: 80 (Studies in Computational Intelligence, 80)

Rule Extraction from Support Vector Machines - Studies 80

Regularization, Optimization, Kernels, and Support Vector Machines (Chapman & Hall/CRC Machine Learning & Pattern Recognition)

RBPK & EuDiC : A Narrative on SVM Efficiency Improvement

Regularization, Optimization, Kernels, and Support Vector Machines (Chapman & Hall/CRC Machine Learning & Pattern Recognition)

The Nature of Statistical Learning Theory (Information Science and Statistics)

Enhancing Kernel Methods for Pattern Classification: Theories and Implementations

Support Vector Machines in Chemistry and Biotechnology - CRC Press

MIT Press Learning with Kernels - Support Vector Machines Book

Digital Signal Processing with Kernel Methods (IEEE Press)

Machine Learning with Python Cookbook: Practical Solutions from Preprocessing to Deep Learning

Pattern Recognition Algorithms for Data Mining - AI & ML

Pearson Machine Learning Foundations Volume 1: Supervised Learning

Supervised and Unsupervised Pattern Recognition: Feature Extraction and Computational Intelligence (Industrial Electronics)

Learning with Fractional Orthogonal Kernel Classifiers in Support Vector Machines: Theory, Algorithms and Applications (Industrial and Applied Mathematics)

MIT Press Foundations of Machine Learning Textbook

Machine Learning Algorithms: Popular algorithms for data science and machine learning, 2nd Edition

Machine Learning from Weak Supervision: An Empirical Risk Minimization Approach (Adaptive Computation and Machine Learning series)

Computational Learning Theory: 4th European Conference, EuroCOLT'99 Nordkirchen, Germany, March 29-31, 1999 Proceedings: 1572 (Lecture Notes in Computer Science, 1572)