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- Title
- Information-theoretics based genetic algorithm: Application to Hopfield's associative memory model of neural networks.
- Creator
- Arredondo, Tomas Vidal., Florida Atlantic University, Neelakanta, Perambur S.
- Abstract/Description
-
This thesis refers to a research addressing the use of information-theoretic techniques in optimizing an artificial neural network (ANN) via a genetic selection algorithm. Pertinent studies address emulating relevant experiments on a test ANN (based on Hopfield's associative memory model) wherein the said optimization is tried with different sets of control parameters. These parameters include a new entity based on the concept of entropy as conceived in the field of information theory. That...
Show moreThis thesis refers to a research addressing the use of information-theoretic techniques in optimizing an artificial neural network (ANN) via a genetic selection algorithm. Pertinent studies address emulating relevant experiments on a test ANN (based on Hopfield's associative memory model) wherein the said optimization is tried with different sets of control parameters. These parameters include a new entity based on the concept of entropy as conceived in the field of information theory. That is, the mutual entropy (Shannon entropy) or information-distance (Kullback-Leibler-Jensen distance) measure between a pair of candidates is considered in the reproduction process of the genetic algorithm (GA) and adopted as a selection-constraint parameter. The research envisaged further includes a comparative analysis of the test results which indicate the importance of proper parameter selection to realize an optimal network performance. It also demonstrates the ability of the concepts proposed here in developing a new neural network approach for pattern recognition problems.
Show less - Date Issued
- 1997
- PURL
- http://purl.flvc.org/fcla/dt/15397
- Subject Headings
- Neuro network (Computer science), Genetic algorithms
- Format
- Document (PDF)
- Title
- Studies on information-theoretics based data-sequence pattern-discriminant algorithms: Applications in bioinformatic data mining.
- Creator
- Arredondo, Tomas Vidal., Florida Atlantic University, Neelakanta, Perambur S., College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
- Abstract/Description
-
This research refers to studies on information-theoretic (IT) aspects of data-sequence patterns and developing thereof discriminant algorithms that enable distinguishing the features of underlying sequence patterns having characteristic, inherent stochastical attributes. The application potentials of such algorithms include bioinformatic data mining efforts. Consistent with the scope of the study as above, considered in this research are specific details on information-theoretics and entropy...
Show moreThis research refers to studies on information-theoretic (IT) aspects of data-sequence patterns and developing thereof discriminant algorithms that enable distinguishing the features of underlying sequence patterns having characteristic, inherent stochastical attributes. The application potentials of such algorithms include bioinformatic data mining efforts. Consistent with the scope of the study as above, considered in this research are specific details on information-theoretics and entropy considerations vis-a-vis sequence patterns (having stochastical attributes) such as DNA sequences of molecular biology. Applying information-theoretic concepts (essentially in Shannon's sense), the following distinct sets of metrics are developed and applied in the algorithms developed for data-sequence pattern-discrimination applications: (i) Divergence or cross-entropy algorithms of Kullback-Leibler type and of general Czizar class; (ii) statistical distance measures; (iii) ratio-metrics; (iv) Fisher type linear-discriminant measure and (v) complexity metric based on information redundancy. These measures are judiciously adopted in ascertaining codon-noncodon delineations in DNA sequences that consist of crisp and/or fuzzy nucleotide domains across their chains. The Fisher measure is also used in codon-noncodon delineation and in motif detection. Relevant algorithms are used to test DNA sequences of human and some bacterial organisms. The relative efficacy of the metrics and the algorithms is determined and discussed. The potentials of such algorithms in supplementing the prevailing methods are indicated. Scope for future studies is identified in terms of persisting open questions.
Show less - Date Issued
- 2003
- PURL
- http://purl.flvc.org/fau/fd/FADT12057
- Subject Headings
- Data mining, Bioinformatics, Discriminant analysis, Information theory in biology
- Format
- Document (PDF)