Results 251 to 260 of about 4,805,610 (298)

On the semi-supervised learning of multi-layered perceptrons

Interspeech 2009, 2009
We present a novel approach for training a multi-layered perceptron (MLP) in a semi-supervised fashion. Our objective function, when optimized, balances training set accuracy with fidelity to a graph-based manifold over all points. Additionally, the objective favors smoothness via an entropy regularizer over classifier outputs as well as ...
Jonathan Malkin, Amarnag Subramanya, Jeff A. Bilmes   +2 more
openaire   +2 more sources

Mandarin tone recognition by multi-layer perceptron

International Conference on Acoustics, Speech, and Signal Processing, 2002
Tone recognition of isolated Mandarin monosyllables using the multilayer perceptron (MLP) model is reported. Ten features extracted from the fundamental frequency and energy contours of a monosyllable are used as the recognition features. The backpropagation algorithm is used to train the internal representation of the MLP.
Pao-Chung Chang, San-Wei Sun, Sin-Horng Chen   +2 more
openaire   +1 more source

Multi-layer perceptron mapping on a SIMD architecture

Proceedings of the 12th IEEE Workshop on Neural Networks for Signal Processing, 2003
An automatic road sign recognition system, A(RS)/sup 2/, is aimed at the detection and recognition of one or more road signs from real-world color images. The authors have proposed an A(RS)/sup 2/ able to detect and extract sign regions from real world scenes on the basis of their color and shape features.
Salvatore Vitabile, Antonio Gentile, G. B. Dammone, Filippo Sorbello   +3 more
openaire   +2 more sources

Assessing the importance of features for multi-layer perceptrons

Neural Networks, 1998
In this paper we establish a mathematical framework in which we develop measures for determining the contribution of individual features to the performance of a classifier. Corresponding to these measures, we design metrics that allow estimation of the importance of features for a specific multi-layer perceptron neural network.
Michael Egmont-Petersen, Jan L. Talmon, Arie Hasman, Anton W. Ambergen   +3 more
openaire   +3 more sources

On the evaluation of relevance learning by a multi-layer perceptron

Proceedings. 2005 IEEE International Joint Conference on Neural Networks, 2005., 2006
In this paper, we introduce a novel method of relevance learning by a multi-layer perceptron. The relevance learning is regarded as learning from the relationship among two or more outputs of the network. The learning network architecture is based on a simple multi-layer perceptron with a modified back-propagation learning algorithm.
Kenji Suzuki 0002, Shuji Hashimoto
openaire   +2 more sources

Clustering with multi-layered perceptron

Pattern Recognition Letters, 2022
Ankita Chatterjee, Jayasree Saha, Jayanta Mukherjee 0001   +2 more
openaire   +1 more source

Cooperative coevolution of generalized multi-layer perceptrons

Neurocomputing, 2004
Abstract In this work we present the cooperative coevolution of multi-layer generalized perceptrons. This model is based on the cooperation of different subpopulations of modules, each one being a generalized multi-layered perceptron. In some previous works we have developed a modular cooperative coevolutive model for evolving multi-layer ...
Nicolás García-Pedrajas, Domingo Ortiz-Boyer, César Hervás-Martínez   +2 more
openaire   +2 more sources

Phonetic classification using multi-layer perceptrons

International Conference on Acoustics, Speech, and Signal Processing, 2002
Several extensions to the authors' previously published results (Proc. IEEE IC ASSP, p.422-5, 1988) on the constrained task of using multilayer perceptrons to classify the vowels in American English spoken by many speakers and excised from continuous speech are described. For vowel classification, the use of linguistic features is investigated. How the
Hong C. Leung, Victor W. Zue
openaire   +1 more source

Can periodic perceptrons replace multi-layer perceptrons?

Pattern Recognition Letters, 2000
Summary: We propose an efficient alternative to multi-layer perceptron: two-layer Periodic Perceptron (PP). We prove then that PP can compute every binary Boolean function, we give an efficient learning algorithm for PP and test it on academic and realistic problems.
openaire   +3 more sources

On Clifford neurons and Clifford multi-layer perceptrons

Neural Networks, 2008
We study the framework of Clifford algebra for the design of neural architectures capable of processing different geometric entities. The benefits of this model-based computation over standard real-valued networks are demonstrated. One particular example thereof is the new class of so-called Spinor Clifford neurons.
Sven Buchholz 0001, Gerald Sommer
openaire   +2 more sources