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Safe inclusion of information about rates of variation in a reinforcement learning algorithm

Improving backpropagation with sliding mode control

Training linear neural network with early stopped learning and ridge estimation

Learnability in sequential RAM-based neural networks

Optimising the widths of radial basis functions

Unsupervised neural network learning for blind sources separation

A fixed-point algorithm for independent component analysis which uses a priori information

Feedforward neural network initialization: an evolutionary approach

Optimization of the combinatorial neural model

On Kolmogorov's superpositions and Boolean functions

Extracting rules from feedforward Boolean neural networks

A neo-fuzzy-neuron with real time training applied to flux observer for an induction motor

Integration of hierarchical censored production rule (HCPR)-based system and neural networks

A neural-network based approach for recognition of pose and motion gestures on a mobile robot

Fault detection and isolation in robotic manipulators and the radial basis function network trained by the Kohonen's self-organizing map

Niches evolving the positioning of a mechanical arm in a 3D environment

Competitive and temporal Hebbian learning for production of robot trajectories

Improving reinforcement learning control via online bilinear action interpolation

A neural classifier employing biased wavelets

Automatic target recognition using a constructive approach

Design of radial basis function network as classifier in face recognition using eigenfaces

RBF neural networks and MTI for text independent speaker identification

CALIBRA for fuzzy concepts

Learning vector quantization in text-independent automatic speaker recognition

A self-organizing map model for analysis of musical time series

A self-organizing algorithm for image compression

An isolated word speech recognition system based on Kohonen neural network

Some results on activation and scaling of sparse distributed memory

A general approach for density estimation in the n-dimensional Boolean space

Convergence in a sparse distributed memory

A realistic computer simulation of primary somatosensory cortex replicating static properties of topographic organization

Implementation of a probabilistic neural network for multi-spectral image classification on an FPGA based custom computing machine

Realization of a mixed-mode neural coprocessor for signal processing

A new proposal for implementation of competitive neural networks in analog hardware

A signal processing system based upon monolithic neural coprocessors

Factor semantics for document retrieval

A neural mapping for improving the performance of a high-energy calorimeter

Self-organizing modeling in forecasting daily river flows

An adaptive neural fuzzy network model for seasonal stream flow forecasting

Evaluation of neural classifiers using statistic methods for identification of laryngeal pathologies

Target recognition using evolutionary neural networks

Artificial neural networks applied to theoretical chemistry

Context and scale influencing clustering through unsupervised neural networks

A neural architecture for the identification of number sequences

Artificial neural network applied to power system protection

A genetic algorithm approach to optimize the operation planning of hydrothermal system scheduling

Index of authors

Proceedings 4th Brazilian Symposium on Neural Networks

Factor semantics for document retrieval

Content Provider	IEEE Xplore Digital Library
Author	Calvo, R.A.
Copyright Year	1998
Description	Author affiliation: Language Technol. Inst., Carnegie Mellon Univ., Pittsburgh, PA, USA (Calvo, R.A.)
Abstract	Principal component analysis is a useful technique for reducing the dimensionality of datasets, a paramount need in high dimensional term spaces. We study three neural networks with Hebbian-like learning that approximately produce the principal components of a document database. The explained variance of the solutions shows how much information the reduced space retains. In this database the first factor is strongly described by words that are usually included in a stop-word list, other factors show high loadings in content specific terms; this indicates that the networks are learning the semantic space. The retrieval performance on the reduced space is compared with other methods.
Starting Page	198
Ending Page	203
File Size	122200
Page Count	6
File Format	PDF
ISBN	0818686294
DOI	10.1109/SBRN.1998.731028
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	1998-12-09
Publisher Place	Brazil
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Feature extraction Principal component analysis Information retrieval Large scale integration Frequency Helium Reactive power Redundancy Thesauri Mutual information
Content Type	Text
Resource Type	Article

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