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Principal Manifolds for Data Visualization and Dimension Reduction
The book starts with the quote of the classical Pearson definition of PCA and includes reviews of various methods: NLPCA, ICA, MDS, embedding and clustering algorithms, principal manifolds and SOM. New approaches to NLPCA, principal manifolds, branching principal components and topology preserving mappings are described. Presentation of algorithms is supplemented by case studies. The volume ends with a tutorial PCA deciphers genome.
Artificial Neural Networks and Machine Learning – ICANN 2024
The ten-volume set LNCS 15016-15025 constitutes the refereed proceedings of the 33rd International Conference on Artificial Neural Networks and Machine Learning, ICANN 2024, held in Lugano, Switzerland, during September 17–20, 2024. The 294 full papers and 16 short papers included in these proceedings were carefully reviewed and selected from 764 submissions. The papers cover the following topics: Part I - theory of neural networks and machine learning; novel methods in machine learning; novel neural architectures; neural architecture search; self-organization; neural processes; novel architectures for computer vision; and fairness in machine learning. Part II - computer vision: classifica...
Intelligent Data Engineering and Automated Learning--IDEAL 2006
This book constitutes the refereed proceedings of the 7th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2006. The 170 revised full papers presented were carefully selected from 557 submissions. The papers are organized in topical sections on learning and information processing, data mining, retrieval and management, bioinformatics and bio-inspired models, agents and hybrid systems, financial engineering, as well as a special session on nature-inspired date technologies.
Intelligent Data Engineering and Automated Learning – IDEAL 2008
This book constitutes the refereed proceedings of the 9th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2008, held in Daejeon, Korea, in November 2008. The 56 revised full papers presented together with 10 invited papers were carefully reviewed and selected from numerous submissions for inclusion in the book. The papers are organized in topical sections on learning and information processing, data mining and information management, bioinformatics and neuroinformatics, agents and distributed systems, as well as financial engineering and modeling.
Advances in Neural Networks - ISNN 2006
Annotation The three volume set LNCS 3971/3972/3973 constitutes the refereed proceedings of the Third International Symposium on Neural Networks, ISNN 2006, held in Chengdu, China in May/June 2006. The 616 revised papers presented were carefully reviewed and selected from about 2.500 submissions. The papers are organized in topical sections on neurobiological analysis, theoretical analysis, neurodynamic optimization, learning algorithms, model design, kernel methods, ICA and BSS, data preprocessing, pattern classification, computer vision, image processing, signal processing, system modeling, control systems, robotic systems, power systems, transportation systems, communication networks, information security, fault detection, financial analysis, neuroinformatics, bioinformatics, biomedical applications, industrial applications, other applications, and hardware implementation.
Data Management for Multimedia Retrieval
Multimedia data require specialised management techniques because the representations of colour, time, semantic concepts, and other underlying information can be drastically different from one another. This textbook on multimedia data management techniques gives a unified perspective on retrieval efficiency and effectiveness. It provides a comprehensive treatment, from basic to advanced concepts, that will be useful to readers of different levels, from advanced undergraduate and graduate students to researchers and to professionals. After introducing models for multimedia data (images, video, audio, text, and web) and for their features, such as colour, texture, shape, and time, the book presents data structures and algorithms that help store, index, cluster, classify, and access common data representations. The authors also introduce techniques, such as relevance feedback and collaborative filtering, for bridging the 'semantic gap' and present the applications of these to emerging topics, including web and social networking.
汉英明清历史档案词典
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