Optimized extreme learning machine binary classification method based on improved active set algorithms

An extreme learning machine and binary classification technology, applied in the field of neural networks, can solve problems such as high number of false iterations and long calculation time

Inactive Publication Date: 2015-08-26
XIAN UNIV OF TECH
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Problems solved by technology

[0008] The purpose of the present invention is to provide a binary classification method for extreme learning machines based on improved effective set algorithm optimization, realize efficient optimization of extreme learning machines, and solve the problems of long calculation time and high number of wrong iterations in the prior art

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  • Optimized extreme learning machine binary classification method based on improved active set algorithms
  • Optimized extreme learning machine binary classification method based on improved active set algorithms
  • Optimized extreme learning machine binary classification method based on improved active set algorithms

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[0054] The present invention will be described in detail below in conjunction with the accompanying drawings and specific embodiments.

[0055] The meaning of the definition symbol is: ||·|| represents the Euclidean norm, and K is derived from K ij An N×N matrix consisting of, i , j = 1 , . . . , N , I ⊆ { 1 , . . . , N } , K II by K ij The composed |I|×|I| sub-matrix, i∈I, j∈I, α is an N-dimensional vector, α I by alpha i composition, i∈I. Gradient▽f(α) represents a row vector, gradient g(α)=▽f(α) T Represents a column vector and T represents the transpose. The superscript k indicates the number of iterations.

[0056] The present invention studies the optimization method of the extreme learning machine used to solve the bin...

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Abstract

The invention discloses an optimized extreme learning machine binary classification method based on improved active set algorithms. The method comprises the following steps of: firstly, providing a training sample set, and determining an optimized solution problem; secondly, calculating a decreasing direction dk according to a BAS active set algorithm, and finding a maximum searching step size without violating constraint conditions along the decrease direction dk; thirdly, setting a temporary iteration step size according to an EAS active set algorithm, and continuously finding an optimal step size; and fourthly, utilizing a prediction assignment method to reduce the calculation cost of iterating a working set variable to the optimal value. An optimized extreme learning machine is adopted to carry out classification on unknown samples.

Description

technical field [0001] The invention belongs to the technical field of neural networks, and in particular relates to an extreme learning machine binary classification method optimized based on an improved effective set algorithm. Background technique [0002] In all aspects of people's daily life, most practical problems can be converted into a classification problem, and the performance of a classifier is often the key to an application result. The research results of machine learning are widely used in classifiers. Therefore, continuously improving the classification performance of classifiers has become the mainstream research direction of machine learning. [0003] Extreme Learning Machine (Extreme Learning Machine, ELM) was proposed by Professor Huang Guangbin of Nanyang Technological University in Singapore. Using extreme learning machine to solve binary classification problems makes the development of artificial intelligence go further. This algorithm is a supervised...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06N3/08G06F17/30
Inventor 赵明华丁晓枫莫瑞阳曹慧原永芹石争浩姚全珠
Owner XIAN UNIV OF TECH
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