Automated feature selection with a distinction sensitive learning vector quantizer
NeurocomputingPublished 1 May 1996
M. Pregenzer, G. Pfurtscheller, D. Flotzinger
Citations81
SJR quartileQ1
SJR score1.47
SNIP1.94
Generate an AI Snapshot to get a quick, structured summary of this paper.
Study Snapshot
ObjectiveStudy objective
MethodsResearch methodology
PopulationPopulation studied
Sample sizeSample sizes
OutcomesStudy outcomes here
ResultsStudy results comes here
LimitationsResearch study limitations comes here
A concise AI-generated summary of the paper will appear here once you click Generate AI Snapshot.
TL;DR
An extended version of Kohonen's LVQ algorithm, called Distinction Sensitive Learning Vector Quantization (DSLVQ), is introduced which overcomes a major problem of LVQ, the dependency on proper pre-processing methods for scaling and feature selection.
Abstract
An extended version of Kohonen's Learning Vector Quantization (LVQ) algorithm, called Distinction Sensitive Learning Vector Quantization (DSLVQ), is introduced which overcomes a major problem of LVQ, the dependency on proper pre-processing methods for scaling and feature selection. The algorithm employs a weighted distance function and adapts the metric with learning. Highest weights are assigned to components in the input vectors which are most informative for classification; non-informative components are discarded. The algorithm is applied to the analyses of multi-channel EEG data and compared with experienced methods.
Keywords
Computer ScienceEngineering
Cognitive ScienceFinding Structure in Time
10,841 Citations1990Jeffrey L. Elman
A proposal along these lines first described by Jordan (1986) which involves the use of recurrent links in order to provide networks with a dynamic memory and suggests a method for representing lexical categories and the type/token distinction is developed.
The MIT Press eBooksParallel Distributed Processing
3,227 Citations1987James L. McClelland, David E. Rumelhart +1 more
Research Showcase @ Carnegie Mellon University (Carnegie Mellon University)The Cascade-Correlation learning architecture
2,401 Citations2018Scott E. Fahlman, Christian Lebière
The Cascade-Correlation architecture has several advantages over existing algorithms: it learns very quickly, the network determines its own size and topology, it retains the structures it has built even if the training set changes, and it requires no back-propagation of error signals through the connections of the network.
Electroencephalography and Clinical NeurophysiologyAn EEG-based brain-computer interface for cursor control
1,122 Citations1991Jonathan R. Wolpaw, Dennis J. McFarland +2 more
Electroencephalography and Clinical NeurophysiologyEvaluation of event-related desynchronization (ERD) preceding and following voluntary self-paced movement
757 Citations1979G. Pfurtscheller, A. Aranibar
A method of accurate storage and on-line preprocessing of an EEG signal, preceding and following a trigger signal, elicited by button pressing, is described to study changes occurring in the power of the rhythmic activity within the alpha band in central areas, during voluntary, self-paced movement in 10 normal humans.
Machine LearningA Weighted Nearest Neighbor Algorithm for Learning with Symbolic Features
663 Citations1993Scott Cost, Steven L. Salzberg
A nearest neighbor algorithm for learning in domains with symbolic features, which produces excellent classification accuracy on three problems that have been studied by machine learning researchers: predicting protein secondary structure, identifying DNA promoter sequences, and pronouncing English text.
ComputerThe 'neural' phonetic typewriter
565 Citations1988Teuvo Kohonen
A speaker-adaptive system that transcribes dictation using an unlimited vocabulary is presented that is based on a neural network processor for the recognition of phonetic units of speech.
Electroencephalography and Clinical NeurophysiologyPatterns of cortical activation during planning of voluntary movement
527 Citations1989G. Pfurtscheller, A. Berghold
The influence of planning of self-paced voluntary finger movements on alpha band components was studied and it is concluded that the side of movement is predetermined more than 1 sec before movement onset.
Journal of Microcomputer ApplicationsBrain-Computer Interface—a new communication device for handicapped persons
500 Citations1993Gert Pfurtscheller, Doris Flotzinger +1 more
First results on a BCI developed in Graz are reported: 85% correct movements can be obtained after only a few days training.
Improved versions of learning vector quantization
497 Citations1990Teuvo Kohonen
It has turned out that the differences between the presented algorithms in regard to the remaining discretization error are not significant, and thus the choice of the algorithm may be based on secondary arguments, such as stability in learning, in which respect the variant introduced (LVQ2.1) seems to be superior to the others.
Statistical pattern recognition with neural networks: benchmarking studies.
404 Citations1988Teuvo Kohonen, G. Barna +1 more
Machine LearningA weighted nearest neighbor algorithm for learning with symbolic features
371 Citations1993Scott Cost, Steven L. Salzberg
A nearest neighbor algorithm for learning in domains with symbolic features, which produces excellent classification accuracy on three problems that have been studied by machine learning researchers: predicting protein secondary structure, identifying DNA promoter sequences, and pronouncing English text.
IEEE International Conference on Neural NetworksStatistical pattern recognition with neural networks: benchmarking studies
349 Citations1988Kohonen, Barna +1 more
Three basic types of neural-like networks, backpropagation network, Boltzmann machine, and learning vector quantization, were applied to two representative artificial statistical pattern recognition tasks, each with varying dimensionality.
Elsevier eBooksSTATISTICAL PATTERN RECOGNITION REVISITED
42 Citations1990Teuvo Kohonen
It has now turned out that certain new self-organizing approaches, here grouped under the title Learning Vector Quantization (LVQ), can combine theoretically near-optimal accuracy with very high speed in learning and recognition and their implementation by analog VLSI is straightforward.
Biomedizinische Technik/Biomedical EngineeringEEG Classification by Learning Vector Quantization - EEG-Klassifikation mit Hilfe eines Learning Vector Quantizers
37 Citations1992Doris Flotzinger, J. Kalcher +1 more
EEG classification using Learning Vector Quantization (LVQ) is introduced on the basis of a Brain-Computer Interface built in Graz, where a subject controlled a cursor in one dimension on a monitor using potentials recorded from the intact scalp.
Artificial Intelligence in MedicineNeural network based classification of single-trial EEG data
36 Citations1993Nikola Mašić, G. Pfurtscheller
Standard Back Propagation, Partially Recurrent and Cascade- Correlation neural networks were used to predict the side of finger movement on the basis of non-averaged single trial multi-channel EEG data recorded prior to movement to show that the Cascade-Correlation neural network is an appropriate choice for neural network based classification of spatio-temporal single-trial EEG patterns.
Microprocessor implementation of a large vocabulary speech recognizer and phonetic typewriter for Finnish and Japanese
31 Citations1987Teuvo Kohonen, K. Torkkola +3 more
A flexible and inexpensive real-time speech recognitlon system that produces orthographically edited text for Finnish and romanized Japanese from the erroneus transcriptions within unlimited vocabulary.