Results 1 - 10 of 846

Evolving Artificial Neural Networks

by Xin Yao , 1999
"... This paper: 1) reviews different combinations between ANN's and evolutionary algorithms (EA's), including using EA's to evolve ANN connection weights, architectures, learning rules, and input features; 2) discusses different search operators which have been used in various EA's; ..."
Abstract - Cited by 574 (6 self) - Add to MetaCart
This paper: 1) reviews different combinations between ANN's and evolutionary algorithms (EA's), including using EA's to evolve ANN connection weights, architectures, learning rules, and input features; 2) discusses different search operators which have been used in various EA

The cascade-correlation learning architecture

by Scott E. Fahlman, Christian Lebiere - Advances in Neural Information Processing Systems 2 , 1990
"... Cascade-Correlation is a new architecture and supervised learning algorithm for artificial neural networks. Instead of just adjusting the weights in a network of fixed topology, Cascade-Correlation begins with a minimal network, then automatically trains and adds new hidden units one by one, creatin ..."
Abstract - Cited by 801 (6 self) - Add to MetaCart
Cascade-Correlation is a new architecture and supervised learning algorithm for artificial neural networks. Instead of just adjusting the weights in a network of fixed topology, Cascade-Correlation begins with a minimal network, then automatically trains and adds new hidden units one by one

A Review of Evolutionary Artificial Neural Networks

by Xin Yao , 1993
"... Research on potential interactions between connectionist learning systems, i.e., artificial neural networks (ANNs), and evolutionary search procedures, like genetic algorithms (GAs), has attracted a lot of attention recently. Evolutionary ANNs (EANNs) can be considered as the combination of ANNs and ..."
Abstract - Cited by 202 (23 self) - Add to MetaCart
Research on potential interactions between connectionist learning systems, i.e., artificial neural networks (ANNs), and evolutionary search procedures, like genetic algorithms (GAs), has attracted a lot of attention recently. Evolutionary ANNs (EANNs) can be considered as the combination of ANNs

Is imitation learning the route to humanoid robots?

by Stefan Schaal - TRENDS IN COGNITIVE SCIENCES , 1999
"... This review investigates two recent developments in artificial intelligence and neural computation: learning from imitation and the development of humanoid robots. It is postulated that the study of imitation learning offers a promising route to gain new insights into mechanisms of perceptual motor ..."
Abstract - Cited by 308 (18 self) - Add to MetaCart
This review investigates two recent developments in artificial intelligence and neural computation: learning from imitation and the development of humanoid robots. It is postulated that the study of imitation learning offers a promising route to gain new insights into mechanisms of perceptual motor

A New Evolutionary System for Evolving Artificial Neural Networks

by Xin Yao, Yong Liu - IEEE TRANSACTIONS ON NEURAL NETWORKS , 1996
"... This paper presents a new evolutionary system, i.e., EPNet, for evolving artificial neural networks (ANNs). The evolutionary algorithm used in EPNet is based on Fogel's evolutionary programming (EP) [1], [2], [3]. Unlike most previous studies on evolving ANNs, this paper puts its emphasis on ev ..."
Abstract - Cited by 202 (35 self) - Add to MetaCart
This paper presents a new evolutionary system, i.e., EPNet, for evolving artificial neural networks (ANNs). The evolutionary algorithm used in EPNet is based on Fogel's evolutionary programming (EP) [1], [2], [3]. Unlike most previous studies on evolving ANNs, this paper puts its emphasis

Artificial Neural Networks: A Tutorial

by Anil K. Jain, Jianchang Mao, K. Mohiuddin - IEEE Computer , 1996
"... Numerous efforts have been made in developing "intelligent" programs based on the Von Neumann's centralized architecture. However, these efforts have not been very successful in building general-purpose intelligent systems. Inspired by biological neural networks, researchers in a numb ..."
Abstract - Cited by 104 (4 self) - Add to MetaCart
number of scientific disciplines are designing artificial neural networks (ANNs) to solve a variety of problems in decision making, optimization, prediction, and control. Artificial neural networks can be viewed as parallel and distributed processing systems which consist of a huge number of simple

Multi-column deep neural networks for image classification

by Dan Ciresan, Ueli Meier, Jürgen Schmidhuber - IN PROCEEDINGS OF THE 25TH IEEE CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR 2012 , 2012
"... Traditional methods of computer vision and machine learning cannot match human performance on tasks such as the recognition of handwritten digits or traffic signs. Our biologically plausible deep artificial neural network architectures can. Small (often minimal) receptive fields of convolutional win ..."
Abstract - Cited by 151 (9 self) - Add to MetaCart
Traditional methods of computer vision and machine learning cannot match human performance on tasks such as the recognition of handwritten digits or traffic signs. Our biologically plausible deep artificial neural network architectures can. Small (often minimal) receptive fields of convolutional

Towards Designing Artificial Neural Networks by Evolution

by Xin Yao, Yong Liu - Applied Mathematics and Computation , 1996
"... Designing artificial neural networks (ANNs) for different applications has been a key issue in the ANN field. At present, ANN design still relies heavily on human experts who have sufficient knowledge about ANNs and the problem to be solved. As ANN's complexity increases, designing ANNs manuall ..."
Abstract - Cited by 65 (12 self) - Add to MetaCart
Designing artificial neural networks (ANNs) for different applications has been a key issue in the ANN field. At present, ANN design still relies heavily on human experts who have sufficient knowledge about ANNs and the problem to be solved. As ANN's complexity increases, designing ANNs

An Efficient Gradient-Based Algorithm for On-Line Training of Recurrent Network Trajectories

by Ronald J. Williams, Jing Peng - Neural Computation , 1990
"... A novel variant of a familiar recurrent network learning algorithm is described. This algorithm is capable of shaping the behavior of an arbitrary recurrent network as it runs, and it is specifically designed to execute efficiently on serial machines. 1 Introduction Artificial neural networks having ..."
Abstract - Cited by 145 (3 self) - Add to MetaCart
A novel variant of a familiar recurrent network learning algorithm is described. This algorithm is capable of shaping the behavior of an arbitrary recurrent network as it runs, and it is specifically designed to execute efficiently on serial machines. 1 Introduction Artificial neural networks

Phoneme Probability Estimation with Dynamic Sparsely Connected Artificial Neural Networks

by Nikko Ström , 1997
"... This paper presents new methods for training large neural networks for phoneme probability estimation. An architecture combining time-delay windows and recurrent connections is used to capture the important dynamic information of the speech signal. Because the number of connections in a fully connec ..."
Abstract - Cited by 35 (1 self) - Add to MetaCart
This paper presents new methods for training large neural networks for phoneme probability estimation. An architecture combining time-delay windows and recurrent connections is used to capture the important dynamic information of the speech signal. Because the number of connections in a fully
Results 1 - 10 of 846