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TNN
2010
234views Management» more  TNN 2010»
8 years 5 months ago
Novel maximum-margin training algorithms for supervised neural networks
This paper proposes three novel training methods, two of them based on the back-propagation approach and a third one based on information theory for Multilayer Perceptron (MLP) bin...
Oswaldo Ludwig, Urbano Nunes
TNN
2010
182views Management» more  TNN 2010»
8 years 5 months ago
A discrete-time neural network for optimization problems with hybrid constraints
Abstract--Recurrent neural networks have become a prominent tool for optimizations including linear or nonlinear variational inequalities and programming, due to its regular mathem...
Huajin Tang, Haizhou Li, Zhang Yi
TNN
2010
173views Management» more  TNN 2010»
8 years 5 months ago
Multiclass relevance vector machines: sparsity and accuracy
Abstract--In this paper we investigate the sparsity and recognition capabilities of two approximate Bayesian classification algorithms, the multi-class multi-kernel Relevance Vecto...
Ioannis Psorakis, Theodoros Damoulas, Mark A. Giro...
TNN
2010
205views Management» more  TNN 2010»
8 years 5 months ago
Behavior-constrained support vector machines for fMRI data analysis
Statistical learning methods are emerging as a valuable tool for decoding information from neural imaging data. The noisy signal and the limited number of training patterns that ar...
Danmei Chen, Sheng Li, Zoe Kourtzi, Si Wu
TNN
2010
139views Management» more  TNN 2010»
8 years 5 months ago
Identification of finite state automata with a class of recurrent neural networks
A class of recurrent neural networks is proposed and proven to be capable of identifying any discrete-time dynamical system. The application of the proposed network is addressed in...
Sung Hwan Won, Iickho Song, Sun-Young Lee, Cheol H...
TNN
2010
173views Management» more  TNN 2010»
8 years 5 months ago
Global synchronization for discrete-time stochastic complex networks with randomly occurred nonlinearities and mixed time delays
Abstract--In this paper, the problem of stochastic synchronization analysis is investigated for a new array of coupled discretetime stochastic complex networks with randomly occurr...
Zidong Wang, Yao Wang, Yurong Liu
TNN
2010
216views Management» more  TNN 2010»
8 years 5 months ago
Simplifying mixture models through function approximation
Finite mixture model is a powerful tool in many statistical learning problems. In this paper, we propose a general, structure-preserving approach to reduce its model complexity, w...
Kai Zhang, James T. Kwok
TNN
2010
127views Management» more  TNN 2010»
8 years 5 months ago
RAMOBoost: ranked minority oversampling in boosting
In recent years, learning from imbalanced data has attracted growing attention from both academia and industry due to the explosive growth of applications that use and produce imba...
Sheng Chen, Haibo He, Edwardo A. Garcia
TNN
2010
171views Management» more  TNN 2010»
8 years 5 months ago
Sensitivity versus accuracy in multiclass problems using memetic Pareto evolutionary neural networks
This paper proposes a multiclassification algorithm using multilayer perceptron neural network models. It tries to boost two conflicting main objectives of multiclassifiers: a high...
Juan Carlos Fernández Caballero, Francisco ...
TNN
2010
159views Management» more  TNN 2010»
8 years 5 months ago
Multiple incremental decremental learning of support vector machines
We propose a multiple incremental decremental algorithm of Support Vector Machine (SVM). Conventional single incremental decremental SVM can update the trained model efficiently w...
Masayuki Karasuyama, Ichiro Takeuchi
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