Titlebook: Efficient Learning Machines; Theories, Concepts, Mariette Awad,Rahul Khanna Book‘‘‘‘‘‘‘‘ 2015 The Editor(s) (if applicable) and The Author

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書目名稱Efficient Learning Machines影響因子(影響力)學科排名




書目名稱Efficient Learning Machines網絡公開度




書目名稱Efficient Learning Machines網絡公開度學科排名




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書目名稱Efficient Learning Machines讀者反饋學科排名

娘娘腔

Machine Learning and Knowledge Discovery, in diverse fields related to engineering, biological science, social media, medicine, and business intelligence. The primary objective for most of the applications is to characterize patterns in a complex stream of data. These patterns are then coupled with knowledge discovery and decision making.

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Support Vector Machines for Classification, learning model. SVM offers a principled approach to problems because of its mathematical foundation in statistical learning theory. SVM constructs its solution in terms of a subset of the training input. SVM has been extensively used for classification, regression, novelty detection tasks, and feat

GIBE

Support Vector Regression, presented in . can be generalized to become applicable to regression problems. As in classification, . (SVR) is characterized by the use of kernels, sparse solution, and VC control of the margin and the number of .. Although less popular than SVM, SVR has been proven to be an effective tool in real

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Deep Neural Networks,ong the many evolutions of ANN, . (DNNs) (Hinton, Osindero, and Teh 2006) stand out as a promising extension of the shallow ANN structure. The best demonstration thus far of hierarchical learning based on DNN, along with other Bayesian inference and deduction reasoning techniques, has been the perfo

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