Titlebook: Innovations in Neural Information Paradigms and Applications; Monica Bianchini,Marco Maggini,Lakhmi C. Jain Book 2009 Springer-Verlag Berl

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A. C. Tsoi,Markus Hagenbuchner,R. Chau,Vincent Leetional scientists.Also available online in LINK: http://www..Critically evaluated experimental data covering the densities of organic compounds are essential for both scientific and industrial applications. Knowledge of densities is important in many areas, including custody transfer of materials, p

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Věra K?rkovátional scientists.Also available online in LINK: http://www..Critically evaluated experimental data covering the densities of organic compounds are essential for both scientific and industrial applications. Knowledge of densities is important in many areas, including custody transfer of materials, p

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Giorgio Gnecco,Marcello Sanguinetitional scientists.Also available online in LINK: http://www..Critically evaluated experimental data covering the densities of organic compounds are essential for both scientific and industrial applications. Knowledge of densities is important in many areas, including custody transfer of materials, p

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Self-Organizing Maps for Structured Domains: Theory, Models, and Learning of Kernels,o produce a topology preserving projection of high dimensional data vectors onto a low dimensional (often 2-dimensional) display space [20]. A SOM is generally trained unsupervised. The computational complexity of the underlying algorithms grows linearly with the size and number of inputs, which ren

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Unsupervised and Supervised Learning of Graph Domains,ure is a symmetrical un-directed graph. An unsupervised learning approach is applied to cluster a given text corpus into groups of similar structured graphs. Moreover, if labels are given to some of the documents in the text corpus, a supervised learning approach can be applied to learn the underlyi

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Neural Grammar Networks,descent in error space (Werbos, 1994) and other methods. These networks have been applied to a wide variety of problems from a broad range of domains that can be formulated as vectorto- vector mappings. That is, the function to be approximated must be represented as a function whose domain and whose

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Regularization and Suboptimal Solutions in Learning from Data,nov, Ivanov, Phillips, and Miller regularizations are analyzed. Theoretical features of the optimization problems associated with these regularization techniques and their use in learning tasks are considered. Weight-decay learning is investigated, too. Exploiting properties of the functionals to be