Titlebook: Meta-Learning in Computational Intelligence; Norbert Jankowski,Włodzisław Duch,Krzysztof Gra̧bc Book 2011 Springer Berlin Heidelberg 2011

冬眠

Book 2011r vision, signal processing or text and multimedia understanding, problems that require deep learning techniques, are open. .Modern data mining packages contain numerous modules for data acquisition, pre-processing, feature selection and construction, instance selection, classification, association

Obsequious

恶心

Choosing the Metric: A Simple Model Approach, similarity with respect to the application should be the optimal one whatever model is used for classification or regression. This idea is tested against nine datasets and five prediction models. The results show that this approach is a reasonable compromise between the default choice and a fully-optimized choice of the metric.

表否定

Selecting Machine Learning Algorithms Using the Ranking Meta-Learning Approach,e correlation between the suggested rankings of algorithms and the ideal rankings. The results revealed that Meta-Learning was able to suggest more adequate rankings in both domains of application considered.

轻浮女

1860-949X ng.Written by leading experts in the field.Computational Intelligence (CI) community has developed hundreds of algorithms for intelligent data analysis, but still many hard problems in computer vision, signal processing or text and multimedia understanding, problems that require deep learning techni

少量

Universal Meta-Learning Architecture and Algorithms,ce, basing on learning from data..The main ideas of our meta-learning algorithms lie in complexity controlled loop, searching for most adequate models and in using special functional specification of search spaces (the meta-learning spaces) combined with flexible way of defining the goal of meta-searching.

propose

Meta-Learning Architectures: Collecting, Organizing and Exploiting Meta-Knowledge,ation, or at least to get proper guidance when applying it, we need to build extended meta-learning systems that encompass the entire knowledge discovery process, from raw data to finished models, and that keep learning, keep accumulating meta-knowledge, every time they are presented with new problems.

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地壳

Self-organization of Supervised Models,n be achieved by an efficient combination of models or classifiers..The increasing popularity of combination (ensembling, blending) of diverse models has been significantly influenced by its success in various data mining competitions [8,38].

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