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» Introduction to Pattern Recognition
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GECCO
2008
Springer
261views Optimization» more  GECCO 2008»
15 years 5 months ago
SSNNS -: a suite of tools to explore spiking neural networks
We are interested in engineering smart machines that enable backtracking of emergent behaviors. Our SSNNS simulator consists of hand-picked tools to explore spiking neural network...
Heike Sichtig, J. David Schaffer, Craig B. Laramee
TCBB
2008
103views more  TCBB 2008»
15 years 3 months ago
DNA Motif Representation with Nucleotide Dependency
The problem of discovering novel motifs of binding sites is important to the understanding of gene regulatory networks. Motifs are generally represented by matrices (PWM or PSSM) o...
Francis Y. L. Chin, Henry C. M. Leung
JGAA
2007
135views more  JGAA 2007»
15 years 3 months ago
Challenging Complexity of Maximum Common Subgraph Detection Algorithms: A Performance Analysis of Three Algorithms on a Wide Dat
Graphs are an extremely general and powerful data structure. In pattern recognition and computer vision, graphs are used to represent patterns to be recognized or classified. Det...
Donatello Conte, Pasquale Foggia, Mario Vento
119
Voted
PRL
2002
70views more  PRL 2002»
15 years 3 months ago
On the use of nearest feature line for speaker identification
As a new pattern classification method, nearest feature line (NFL) provides an effective way to tackle the sort of pattern recognition problems where only limited data are availab...
Ke Chen 0001, Ting-Yao Wu, HongJiang Zhang
118
Voted
ICPR
2010
IEEE
15 years 1 months ago
Rectifying Non-Euclidean Similarity Data Using Ricci Flow Embedding
Similarity based pattern recognition is concerned with the analysis of patterns that are specified in terms of object dissimilarity or proximity rather than ordinal values. For man...
Weiping Xu, Edwin R. Hancock, Richard C. Wilson