کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
411054 679177 2010 9 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Optimization method based extreme learning machine for classification
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر هوش مصنوعی
پیش نمایش صفحه اول مقاله
Optimization method based extreme learning machine for classification
چکیده انگلیسی

Extreme learning machine (ELM) as an emergent technology has shown its good performance in regression applications as well as in large dataset (and/or multi-label) classification applications. The ELM theory shows that the hidden nodes of the “generalized” single-hidden layer feedforward networks (SLFNs), which need not be neuron alike, can be randomly generated and the universal approximation capability of such SLFNs can be guaranteed. This paper further studies ELM for classification in the aspect of the standard optimization method and extends ELM to a specific type of “generalized” SLFNs—support vector network. This paper shows that: (1) under the ELM learning framework, SVM's maximal margin property and the minimal norm of weights theory of feedforward neural networks are actually consistent; (2) from the standard optimization method point of view ELM for classification and SVM are equivalent but ELM has less optimization constraints due to its special separability feature; (3) as analyzed in theory and further verified by the simulation results, ELM for classification tends to achieve better generalization performance than traditional SVM. ELM for classification is less sensitive to user specified parameters and can be implemented easily.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Neurocomputing - Volume 74, Issues 1–3, December 2010, Pages 155–163
نویسندگان
, , ,