Titlebook: New Learning Paradigms in Soft Computing; Lakhmi C. Jain,Janusz Kacprzyk Book 2002 Springer-Verlag Berlin Heidelberg 2002 Lazy learning.ar

評(píng)論者

María C. Fernández-Baizán,Ernestina Menasalvas Ruiz,Juan Francisco Martínez Sarríasdidaktisch gut aufbereitet.Includes supplementary material: .Der Fokus des Buches liegt auf dem Tragwerksentwurf und der konstruktiven Durchbildung der Stahl- und Verbundkonstruktionen. In einer ganzheitlichen Betrachtungsweise werden dabei nicht nur statisch konstruktive Eigenschaften der Stahl- un

柏樹(shù)

Statistical Learning by Natural Gradient Descent,discovered an efficient scheme to represent the Fisher information matrix of a stochastic two-layer perceptron. Based on this scheme, we have designed an algorithm to compute the natural gradient. When the input dimension . is much larger than the number of hidden neurons, the complexity of this alg

激怒

incarcerate

微粒

canvass

濕潤(rùn)

Knowledge Extraction from Reinforcement Learning,ion of explicit, symbolic rules from neural reinforcement learners, and the extraction of complete plans from such learners. The advantages of such knowledge extraction include (1) the improvement of learning (especially with the rule extraction approach), and (2) the improvement of the usability of

貪婪地吃

gait-cycle

Performance Comparisons of Neural Networks and Machine Learning Techniques: A Critical Assessment oion of the statistical evaluation of experiments. This chapter provides a short review of the main ideas of such studies and their statistical evaluation. Further the chapter presents empirical results suggesting that the achievable statistical validity of many studies of the style done in the past

promote

Digital Systems Design Through Learning,damental neural architectures, learning strategies and interpretation of their results are presented. We promote a concept of embedding principle: an original Boolean problem is represented in the language of fuzzy sets, afterwards solved through learning, and, finally, the result of learning re-int