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Free eBook Multi-Objective Machine Learning (Studies in Computational Intelligence) download

by Yaochu Jin

Free eBook Multi-Objective Machine Learning (Studies in Computational Intelligence) download ISBN: 3540306765
Author: Yaochu Jin
Publisher: Springer; 2006 edition (March 14, 2006)
Language: English
Pages: 660
Category: Technologies and Future
Subcategory: Computer Science
Size MP3: 1318 mb
Size FLAC: 1944 mb
Rating: 4.5
Format: rtf txt lit doc


Book Condition: Versand aus Deutschland, We dispatch from Germany via Air Mail. Series: Studies in Computational Intelligence (Book 16).

Book Condition: Versand aus Deutschland, We dispatch from Germany via Air Mail. Einband bestoßen, daher Mängelexemplar gestempelt, sonst sehr guter Zustand. Imperfect copy due to slightly bumped cover, apart from this in very good condition. We have a lot of experience in international shipping. Hardcover: 660 pages.

Multi-Objective Machine Learning. It has been shown that the multi-objective approach to machine learning is particularly successful to improve the performance of the traditional single objective machine learning methods, to generate highly diverse multiple Pareto-optimal models for constructing ensembles models and, and to achieve a desired trade-off between accuracy and interpretability of neural networks or fuzzy systems.

Multi-Objective Machine Learning It has been shown that the multi-objective approach to machine learning is particularly successful to improve the performance of the traditional single objective machine.

Электронная книга "Multi-Objective Machine Learning", Yaochu Jin. Эту книгу можно прочитать в Google Play Книгах на компьютере, а также на устройствах Android и iOS. Выделяйте текст, добавляйте закладки и делайте заметки, скачав книгу ". Выделяйте текст, добавляйте закладки и делайте заметки, скачав книгу "Multi-Objective Machine Learning" для чтения в офлайн-режиме.

Multi-Objective Machine Learning Yaochu Jin Springer 9783642067969 .

A comprehensive introduction to machine learning that uses probabilistic models and inference as a unifying approach. Today's Web-enabled deluge of electronic data calls for automated methods of data analysis.

Multi-Objective Machine Learning book. Multi-Objective Machine Learning (Studies in Computational Intelligence) (Studies in Computational Intelligence). 3540306765 (ISBN13: 9783540306764). Recently, increasing interest has been shown in applying.

Machine learning is inherently a multi-objective task. Traditionally, however, either only one of the objectives is adopted as the cost function or multiple objectives are aggregated to a scalar cost function. This can mainly attributed to the fact that most conventional learning algorithms can only deal with a scalar cost function.

Studies in Computational Intelligence. Free delivery worldwide. Multi-Objective Neural Network Optimization for Visual Object Detection.

3. Multi-Objective Machine Learning. Yaochu Jin. Published by Springer (2010). ISBN 10: 3642067964 ISBN 13: 9783642067969. Recently, increasing interest has been shown in applying the concept of Pareto-optimality to machine learning, particularly inspired by the successful developments in evolutionary multi-objective optimization.

Recently, increasing interest has been shown in applying the concept of Pareto-optimality to machine learning, particularly inspired by the successful developments in evolutionary multi-objective optimization. It has been shown that the multi-objective approach to machine learning is particularly successful to improve the performance of the traditional single objective machine learning methods, to generate highly diverse multiple Pareto-optimal models for constructing ensembles models and, and to achieve a desired trade-off between accuracy and interpretability of neural networks or fuzzy systems. This monograph presents a selected collection of research work on multi-objective approach to machine learning, including multi-objective feature selection, multi-objective model selection in training multi-layer perceptrons, radial-basis-function networks, support vector machines, decision trees, and intelligent systems.