Neural Networks
Smooth on-line learning algorithms for hidden Markov models
Neural Computation
Knowledge-based artificial neural networks
Artificial Intelligence
Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence
Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence
Bioinformatics: the machine learning approach
Bioinformatics: the machine learning approach
Scaling Boosting by Margin-Based Inclusionof Features and Relations
ECML '02 Proceedings of the 13th European Conference on Machine Learning
Hidden Markov Model} Induction by Bayesian Model Merging
Advances in Neural Information Processing Systems 5, [NIPS Conference]
New Methods for Splice Site Recognition
ICANN '02 Proceedings of the International Conference on Artificial Neural Networks
Neural Networks for Adaptive Processing of Structured Data
ICANN '01 Proceedings of the International Conference on Artificial Neural Networks
Evaluation of Techniques for Classifying Biological Sequences
PAKDD '02 Proceedings of the 6th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining
How to make large self-organizing maps for nonvectorial data
Neural Networks - New developments in self-organizing maps
Recursive self-organizing maps
Neural Networks - New developments in self-organizing maps
Online algorithm for the self-organizing map of symbol strings
Neural Networks - 2004 Special issue: New developments in self-organizing systems
Fast algorithm and implementation of dissimilarity self-organizing maps
Neural Networks - 2006 Special issue: Advances in self-organizing maps--WSOM'05
Hybrid modeling, hmm/nn architectures, and protein applications
Neural Computation
A Hybrid Self-Organizing Model for Sequence Analysis
ICTAI '08 Proceedings of the 2008 20th IEEE International Conference on Tools with Artificial Intelligence - Volume 02
Exploiting data topology in visualization and clustering of self-organizing maps
IEEE Transactions on Neural Networks
Clustering: A neural network approach
Neural Networks
Prediction of Chatter in Machining Process Based on Hybrid SOM-DHMM Architecture
ICIC '07 Proceedings of the 3rd International Conference on Intelligent Computing: Advanced Intelligent Computing Theories and Applications. With Aspects of Artificial Intelligence
Self-organizing mixture models
Neurocomputing
Graph based representations of density distribution and distances for self-organizing maps
IEEE Transactions on Neural Networks
Topographic mapping of large dissimilarity data sets
Neural Computation
Self-organizing maps, vector quantization, and mixture modeling
IEEE Transactions on Neural Networks
A self-organizing map for adaptive processing of structured data
IEEE Transactions on Neural Networks
Survey of clustering algorithms
IEEE Transactions on Neural Networks
Automatic Cluster Detection in Kohonen's SOM
IEEE Transactions on Neural Networks
Gradient descent learning algorithm overview: a general dynamical systems perspective
IEEE Transactions on Neural Networks
A nonlinear projection method based on Kohonen's topology preserving maps
IEEE Transactions on Neural Networks
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A hybrid approach combining the Self-Organizing Map (SOM) and the Hidden Markov Model (HMM) is presented. The Self-Organizing Hidden Markov Model Map (SOHMMM) establishes a cross-section between the theoretic foundations and algorithmic realizations of its constituents. The respective architectures and learning methodologies are fused in an attempt to meet the increasing requirements imposed by the properties of deoxyribonucleic acid (DNA), ribonucleic acid (RNA), and protein chain molecules. The fusion and synergy of the SOM unsupervised training and the HMM dynamic programming algorithms bring forth a novel on-line gradient descent unsupervised learning algorithm, which is fully integrated into the SOHMMM. Since the SOHMMM carries out probabilistic sequence analysis with little or no prior knowledge, it can have a variety of applications in clustering, dimensionality reduction and visualization of large-scale sequence spaces, and also, in sequence discrimination, search and classification. Two series of experiments based on artificial sequence data and splice junction gene sequences demonstrate the SOHMMM's characteristics and capabilities.