نتایج جستجو برای: extreme learning machine

تعداد نتایج: 819581  

Journal: :Neurocomputing 2014
Huijuan Lu Chun-lin An Enhui Zheng Yi Lu

Extreme Learning Machine (ELM) has salient features such as fast learning speed and excellent generalization performance. However, a single extreme learning machine is unstable in data classification. To overcome this drawback, more and more researchers consider using ensemble of ELMs. This paper proposes a method integrating voting-based extreme learning machines (V-ELM) with dissimilarity (D-...

2016
Ying Yin Yuhai Zhao Chengguang Li Bin Zhang

Multi-instance multi-label learning is a learning framework, where every object is represented by a bag of instances and associated with multiple labels simultaneously. The existing degeneration strategy-based methods often suffer from some common drawbacks: (1) the user-specific parameter for the number of clusters may incur the effective problem; (2) SVM may bring a high computational cost wh...

2013
Pablo Escandell-Montero José María Martínez-Martínez José David Martín-Guerrero Emilio Soria-Olivas Juan Gómez-Sanchís

This paper proposes a least-squares temporal difference (LSTD) algorithm based on extreme learning machine that uses a singlehidden layer feedforward network to approximate the value function. While LSTD is typically combined with local function approximators, the proposed approach uses a global approximator that allows better scalability properties. The results of the experiments carried out o...

Journal: :Pattern Recognition Letters 2014
Wentao Zhu Jun Miao Laiyun Qing

Extreme Support Vector Machine (ESVM) is a nonlinear robust SVM algorithm based on regularized least squares optimization for binary-class classification. In this paper, a novel algorithm for regression tasks, Extreme Support Vector Regression (ESVR), is proposed based on ESVM. Moreover, kernel ESVR is suggested as well. Experiments show that, ESVR has a better generalization than some other tr...

2015
Songyan Huang Chunguang Li Guanrong Chen C. K. Michael Tse Mustak E. Yalcin Hai Yu Mattia Frasca

Distributed data collection and analysis over a network are ubiquitous, especially over a wireless sensor network (WSN). To our knowledge, the data model used in most of the distributed algorithms is linear. However, in real applications, the linearity of systems is not always guaranteed. In nonlinear cases, the single hidden layer feedforward neural network (SLFN) with radial basis function (R...

Journal: :Entropy 2015
Songyan Huang Chunguang Li

Distributed data collection and analysis over a network are ubiquitous, especially over a wireless sensor network (WSN). To our knowledge, the data model used in most of the distributed algorithms is linear. However, in real applications, the linearity of systems is not always guaranteed. In nonlinear cases, the single hidden layer feedforward neural network (SLFN) with radial basis function (R...

2017
Nouar AlDahoul Zaw Zaw Htike Rini Akmeliawati

The objective of goal localization is to find the location of goals in noisy environments. Simple actions are performed to move the agent towards the goal. The goal detector should be capable of minimizing the error between the predicted locations and the true ones. Few regions need to be processed by the agent to reduce the computational effort and increase the speed of convergence. In this pa...

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