عنوان

Multiview machine learning /

(Number (ISBN

9789811330285

(Number (ISBN

9789811330292

(Number (ISBN

9789811330308

(Number (ISBN

981133028X

(Number (ISBN

9811330298

(Number (ISBN

9811330301

Erroneous ISBN

9789811330285

Title Proper

Multiview machine learning /

General Material Designation

[Book]

First Statement of Responsibility

Shiliang Sun, Liang Mao, Ziang Dong, Lidan Wu.

Place of Publication, Distribution, etc.

Singapore :

Name of Publisher, Distributor, etc.

Springer,

Date of Publication, Distribution, etc.

2019.

Specific Material Designation and Extent of Item

1 online resource (x, 149 pages) :

Other Physical Details

illustrations (some color)

Text of Note

Includes bibliographical references.

Text of Note

Intro; Preface; Contents; 1 Introduction; 1.1 Background; 1.2 Definition of Multiview Machine Learning and Related Concepts; 1.3 Typical Application Fields in Artificial Intelligence; 1.4 Why Can Multiview Learning Be Useful; 1.5 Book Structure; References; 2 Multiview Semi-supervised Learning; 2.1 Introduction; 2.2 Co-training Style Methods; 2.2.1 Co-training; 2.2.2 Co-EM; 2.2.3 Robust Co-training; 2.3 Co-regularization Style Methods; 2.3.1 Co-regularization; 2.3.2 Bayesian Co-training; 2.3.3 Multiview Laplacian SVM; 2.3.4 Multiview Laplacian Twin SVM; 2.4 Other Methods; References

Text of Note

3 Multiview Subspace Learning3.1 Introduction; 3.2 Canonical Correlation Analysis and Related Methods; 3.2.1 Canonical Correlation Analysis; 3.2.2 Kernel Canonical Correlation Analysis; 3.2.3 Probabilistic Canonical Correlation Analysis; 3.2.4 Bayesian Canonical Correlation Analysis; 3.3 Multiview Subspace Learning with Supervision; 3.3.1 Multiview Linear Discriminant Analysis; 3.3.2 Multiview Uncorrelated Linear Discriminant Analysis; 3.3.3 Hierarchical Multiview Fisher Discriminant Analysis; 3.4 Other Methods; References; 4 Multiview Supervised Learning; 4.1 Introduction

Text of Note

4.2 Multiview Large Margin Classifiers4.2.1 SVM-2K; 4.2.2 Multiview Maximum Entropy Discriminant; 4.2.3 Soft Margin-Consistency-Based Multiview Maximum Entropy Discrimination; 4.3 Multiple Kernel Learning; 4.3.1 Kernel Combination; 4.3.2 Linear Combination of Kernels and Support Kernel Machine; 4.3.3 SimpleMKL; 4.4 Multiview Probabilistic Models; 4.4.1 Multiview Regularized Gaussian Processes; 4.4.2 Sparse Multiview Gaussian Processes; 4.5 Other Methods; References; 5 Multiview Clustering; 5.1 Introduction; 5.2 Multiview Spectral Clustering; 5.2.1 Co-trained Spectral Clustering

Text of Note

5.2.2 Co-regularized Spectral Clustering5.3 Multiview Subspace Clustering; 5.3.1 Multiview Clustering via Canonical Correlation Analysis; 5.3.2 Multiview Subspace Clustering; 5.3.3 Joint Nonnegative Matrix Factorization; 5.4 Distributed Multiview Clustering; 5.5 Multiview Clustering Ensemble; 5.6 Other Methods; References; 6 Multiview Active Learning; 6.1 Introduction; 6.2 Co-testing; 6.3 Bayesian Co-training; 6.4 Multiple-View Multiple-Learner; 6.5 Active Learning with Extremely Spare Labeled Examples; 6.6 Combining Active Learning with Semi-supervising Learning; 6.7 Other Methods

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Text of Note

This book provides a unique, in-depth discussion of multiview learning, one of the fastest developing branches in machine learning. Multiview Learning has been proved to have good theoretical underpinnings and great practical success. This book describes the models and algorithms of multiview learning in real data analysis. Incorporating multiple views to improve the generalization performance, multiview learning is also known as data fusion or data integration from multiple feature sets. This self-contained book is applicable for multi-modal learning research, and requires minimal prior knowledge of the basic concepts in the field. It is also a valuable reference resource for researchers working in the field of machine learning and also those in various application domains.

Source for Acquisition/Subscription Address

Springer Nature

Stock Number

com.springer.onix.9789811330292

Title

Multiview Machine Learning.

International Standard Book Number

9789811330285

Machine learning.

Machine learning.

COM004000

UYQ

UYQ

Number

Edition

Class number

Sun, Shiliang

Dong, Ziang

Mao, Liang

Wu, Lidan

Date of Transaction

20200823234831.0

Cataloguing Rules (Descriptive Conventions))

pn

Electronic name

[Book]

Y