2 edition of **Kernel-based Data Fusion for Machine Learning** found in the catalog.

- 90 Want to read
- 20 Currently reading

Published
**2011**
by Springer Berlin Heidelberg in Berlin, Heidelberg
.

Written in English

- Engineering,
- Artificial intelligence,
- Bioinformatics

**Edition Notes**

Statement | by Shi Yu, Léon-Charles Tranchevent, Bart Moor, Yves Moreau |

Series | Studies in Computational Intelligence -- 345 |

Contributions | Tranchevent, Léon-Charles, Moor, Bart, Moreau, Yves, SpringerLink (Online service) |

The Physical Object | |
---|---|

Format | [electronic resource] : |

ID Numbers | |

Open Library | OL25545477M |

ISBN 10 | 9783642194054, 9783642194061 |

This book provides a specific introduction to solve data fusion problems using support vector machines. In the first part, this book begins with a brief survey of additive models and Rayleigh . The new fusion algorithm based on training a machine learning classifier over a set containing some frame energy test statistics along with their corresponding decisions .

Multiple kernel learning refers to a set of machine learning methods that use a predefined set of kernels and learn an optimal linear or non-linear combination of kernels as part of the algorithm. Reasons to use multiple kernel learning . Organized in 5 parts, from Fundamental Principles of Remote Sensing; Feature Extraction for Remote Sensing; Image and Data Fusion for Remote Sensing; Integrated Data Merging, Data Reconstruction, Data Fusion, and Machine Learning; to Remote Sensing for Environmental Decision Analysis, the book Cited by: 9.

In machine learning, kernel methods are a class of algorithms for pattern analysis, whose best known member is the support vector machine (SVM). The general task of pattern analysis is to find and study general types of relations (for example clusters, rankings, principal components, correlations, classifications) in many algorithms that solve these tasks, the data . Support Vector Machines are a system for efficiently training the linear learning machines introduced in Chapter 2 in the kernel-induced feature spaces described in Chapter 3, while Cited by:

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This book provides a specific introduction to data fusion problems using support vector machines. In the first part, this book begins with a brief survey of additive models and Rayleigh quotient objectives in machine learning, and then introduces kernel fusion as the additive expansion of support vector machines Cited by: This book provides a specific introduction to data fusion problems using support vector machines.

In the first part, this book begins with a brief survey of additive models and Rayleigh quotient objectives in machine learning, and then introduces kernel fusion as the additive expansion of support vector machines.

Data fusion problems arise frequently in many different fields. This book provides a specific introduction to data fusion problems using support vector machines.

In the first part, this book begins with a brief survey of additive models and Rayleigh quotient objectives in machine learning, and then introduces kernel fusion as the additive expansion of support vector machines. springer, Data fusion problems arise frequently in many different fields.

This book provides a Kernel-based Data Fusion for Machine Learning book introduction to data fusion problems using support vector machines. In the first part, this book begins with a brief survey of additive models and Rayleigh quotient objectives in machine learning, and then introduces kernel fusion as the additive expansion of support vector machines.

Kernel-based Data Fusion for Machine Learning: Methods and Applications in Bioinformatics and Text Mining. Data fusion problems arise frequently in many different fields.

This book. Data fusion problems arise frequently in many different fields. This book provides a specific introduction to data fusion problems using support vector machines. In the first part, this book begins with a brief survey of additive models and Rayleigh quotient objectives in machine learning, and then introduces kernel fusion.

Book Description. Containing numerous algorithms and major theorems, this step-by-step guide covers the fundamentals of kernel-based learning theory. Including over two hundred Cited by: Using the dual representations, the task of learning with multiple data sources is related to the kernel based data fusion, which has been actively studied in the recent ﬁve years.

In the second part of the book, we create several novel algorithms for su- pervised learning and unsupervised learning. The research described in this book covers a number of topics which are relevant to supervised and unsupervised learning by kernel-based data fusion.

The discussion of these topics. |a Kernel-based data fusion for machine learning: |b methods and applications in bioinformatics and text mining / |c Shi Yu [and others].

|a New York: |b Springer, |c Kernel-based data fusion for machine learning:based data fusion for machine learning: methods and applications in bioinformatics and text mining Shi Yu Jury: Prof.

Hens. Data fusion problems arise frequently in many different fields. This book provides a specific introduction to data fusion problems using support vector machines. In the first Kernel-based Data Fusion for Machine Learning: Methods and Kernel-based Data Fusion for Machine Learning.

To tackle the cross-location human activity recognition problem, a fast and simple adaptive mixed and reduced kernel based extreme learning machine (M-RKELM) model has been proposed in Cited by: functions deﬁned on nonvectorial data.

We cover a wide range of methods, ranging from binary classiﬁers to sophisticated methods for estimation with structured data. Introduction. Over the last ten years estimation and learning meth-ods utilizing positive deﬁnite kernels have become rather popular, particu-larly in machine learning.

Data fusion by using machine learning and computational intelligence techniques for medical image analysis and classification.

January Read More. are investigated for model learning using and integrating computational intelligence and machine learning. Kernel-based data fusion for gene prioritization Tijl De Bie.

Department of Engineering Mathematics, we formulate the problem in machine learning terms, Kernel-based data fusion Cited by: Kernel-Based Data Fusion for Machine Learning_ Methods and Applications in Bioinformatics and Text : Add files via upload: Feb 3, LNAI _ Statistical Learning and Data Sciences [Gammerman, Vovk _ Papadopoulos ].pdf: Learning scikit-learn_ Machine Learning.

Data fusion problems arise in many different fields. This book provides a specific introduction to solve data fusion problems using support vector machines. The reader will require a good. Kernel Methods and Machine Learning Offering a fundamental basis in kernel-based learning theory, this book covers both statistical and algebraic principles.

It provides over 30 major theorems for kernel-based supervised and unsupervised learning. Kernel fusion and feature selection in machine learning. In machine learning, when the kernel-based approach is used for estimating dependences in a set of entities, in particular, for. Kernel-based data fusion for machine learning: methods and applications in bioinformatics and text mining.

[Shi Yu;] -- Data fusion problems arise frequently in many different fields. ¡This .Kernel-based data fusion, denoted as kernel fusion, has been pioneered by Lanckriet et al. as a statistical learning framework for genomic data fusion and has been applied widely in various applications.

In particular, kernel representation resolves the heterogeneities of data sources by transforming different data Author: Suhuai Luo, Samar M. Alqhtani, Jiaming Li.representation learning task as deep feature fusion.

We introduce the kernel dropout regularization to enable robust feature learning with kernels from limited data. We develop M-DKMO, a multiple kernel variant of the proposed algorithm, to effectively perform multiple kernel learning File Size: 3MB.