Regularization, Optimization, Kernels, and Support Vector Machines (Chapman & Hall/CRC Machine Learning & Pattern Recognition) - Image 1

£45.05

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

Name: Regularization, Optimization, Kernels, and Support Vector Machines (Chapman & Hall/CRC Machine Learning & Pattern Recognition)
Brand: CRC Press
Price: 41.39 GBP
Availability: InStock

Data Mining

Price History & Forecast

Last 77 days • 77 data points (No recent data available)

Historical

Generating forecast...

Price Distribution

Price distribution over 77 days • 3 price levels

Days at Price

Current Price

Price Analysis

Most common price: £41 (61 days, 79.2%)

Price range: £41 - £47

Price levels: 3 different prices over 77 days

Description

Regularization, Optimization, Kernels, and Support Vector Machines offers a snapshot of the current state of the art of large-scale machine learning, providing a single multidisciplinary source for the latest research and advances in regularization, sparsity, compressed sensing, convex and large-scale optimization, kernel methods, and support vector machines. Consisting of 21 chapters authored by leading researchers in machine learning, this comprehensive reference: Covers the relationship between support vector machines (SVMs) and the Lasso Discusses multi-layer SVMs Explores nonparametric feature selection, basis pursuit methods, and robust compressive sensing Describes graph-based regularization methods for single- and multi-task learning Considers regularized methods for dictionary learning and portfolio selection Addresses non-negative matrix factorization Examines low-rank matrix and tensor-based models Presents advanced kernel methods for batch and online machine learning, system identification, domain adaptation, and image processing Tackles large-scale algorithms including conditional gradient methods, (non-convex) proximal techniques, and stochastic gradient descent Regularization, Optimization, Kernels, and Support Vector Machines is ideal for researchers in machine learning, pattern recognition, data mining, signal processing, statistical learning, and related areas.

Product Specifications

Brand: CRC Press
Format: paperback
ASIN: 0367658984
Category: Books > Subjects > Computing & Internet > Databases > Data Storage & Management > Data Mining
Domain: Amazon UK
Release Date: 30 September 2020
Listed Since: 24 July 2020

Barcode

No barcode data available

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

Price History & Forecast

Price Distribution

Price Analysis

Description

Product Specifications

Barcode

Similar Products You Might Like

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

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

Enhancing Kernel Methods for Pattern Classification: Theories and Implementations

Machine Learning: A Practical Approach on the Statistical Learning Theory

Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond (Adaptive Computation and Machine Learning series)

Machine Learning: A Constraint-Based Approach

Learning Kernel Classifiers: Theory and Algorithms (Adaptive Computation and Machine Learning series)

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

Springer Support Vector Machines for Pattern Classification

Learning with Submodular Functions: A Convex Optimization Perspective (Foundations and Trends® in Machine Learning)

Linear Algebra and Optimization for Machine Learning: A Textbook

Applied Machine Learning Using mlr3 in R

CRC Press - Support Vector Machines in Chemistry and Biotech

Machine Learning: Theory and Practice

RBPK & EuDiC : A Narrative on SVM Efficiency Improvement

A Computational Approach to Statistical Learning (Chapman & Hall/CRC Texts in Statistical Science)

Kernel Mode Decomposition and the Programming of Kernels: 8 (Surveys and Tutorials in the Applied Mathematical Sciences, 8)

Linear Algebra and Optimization for Machine Learning: A Textbook

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

The Mathematics of Machine Learning: Lectures on Supervised Methods and Beyond (De Gruyter Textbook)

Foundations of Machine Learning (Adaptive Computation and Machine Learning series)

Kernel Methods and Machine Learning

Kernel-based Data Fusion for Machine Learning: Methods and Applications in Bioinformatics and Text Mining: 345 (Studies in Computational Intelligence, 345)

Statistical Learning with Sparsity: The Lasso and Generalizations (Chapman & Hall/CRC Monographs on Statistics and Applied Probability)