Proceedings Article10.1109/SIBGRAPI.2008.35
A New Training Algorithm for Pattern Recognition Technique Based on Straight Line Segments
J.H.B. Ribeiro,Ronaldo Fumio Hashimoto +1 more
- 12 Oct 2008
- pp 19-26
TL;DR: This paper presents a new and improved training algorithm for the SLS technique based on gradient descent optimization method and applies it to artificial and public data sets and their results confirm the improvement of this methodology.
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Abstract: Recently, a new pattern recognition technique based on straight line segments (SLSs) was presented. The key issue in this new technique is to find a function based on distances between points and two sets of SLSs that minimizes a certain error or risk criterion. An algorithm for solving this optimization problem is called training algorithm. Although this technique seems to be very promising, the first presented training algorithm is based on a heuristic. In fact, the search for this best function is a hard nonlinear optimization problem. In this paper, we present a new and improved training algorithm for the SLS technique based on gradient descent optimization method. We have applied this new training algorithm to artificial and public data sets and their results confirm the improvement of this methodology.
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Citations
Combining Dialectical Optimization and Gradient Descent Methods for Improving the Accuracy of Straight Line Segment Classifiers
Rosario Medina Rodriguez,Ronaldo Fumio Hashimoto +1 more
- 28 Aug 2011
TL;DR: This paper proposes a combining method of the dialectical optimization method (DOM) and the gradient descent technique for solving this optimization problem and shows that the proposed algorithm has higher classification rates with respect to single gradient descent method and the combination of gradient descent with genetic algorithms.
2
•Proceedings Article
On the use of nearest feature line for speaker identification
Tingyao Wu,Ke Chen +1 more
- 01 Jan 2001
TL;DR: In this paper, the authors explored the use of NFL for speaker identification in terms of limited data and examined how the NFL performs in such a vexing problem of various mismatches between training and test.
1
Evaluation of the Impact of Initial Positions obtained by Clustering Algorithms on the Straight Line Segments Classifier
Rosario Medina-Rodríguez,Cesar Beltran Castanon,Ronaldo Fumio Hashimoto +2 more
- 01 Nov 2018
TL;DR: The results suggest that with an increased noise level, the classification rate of the SLS classifier decreases, however, such reduction was not significant as compared when using the random initial positions.
1
Pattern Recognition Based on Straight Line Segments
Joao Henrique Burckas Ribeiro,Ronaldo Fumio Hashimoto +1 more
- 01 Feb 2010
TL;DR: A new method called Straight Line Segments (SLS) which takes advantage of some good caracteriscs of LVQ and NFL with low computational complexity (lower than Support Vector Machines) (Ribeiro & Hashimoto, 2006; 2008).
1
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Data clustering: a review
TL;DR: An overview of pattern clustering methods from a statistical pattern recognition perspective is presented, with a goal of providing useful advice and references to fundamental concepts accessible to the broad community of clustering practitioners.