Using Genetic Algorithms for Concept Learning
Machine Learning - Special issue on genetic algorithms
CLIP: concept learning from inference patterns
Artificial Intelligence - Special issue: AI research in Japan
Contender's network, a new competitive-learning scheme
Pattern Recognition Letters
Automatic subspace clustering of high dimensional data for data mining applications
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Data equalisation with evidence combination for pattern recognition
Pattern Recognition Letters
Function approximation with decomposed fuzzy systems
Fuzzy Sets and Systems - Special issue on analytical and structural considerations in fuzzy modeling
A note on universal approximation by hierarchical fuzzy systems
Information Sciences: an International Journal - Special issue analytical theory of fuzzy control with applications
BIRCH: A New Data Clustering Algorithm and Its Applications
Data Mining and Knowledge Discovery
AI '02 Proceedings of the 15th Australian Joint Conference on Artificial Intelligence: Advances in Artificial Intelligence
A complete fuzzy decision tree technique
Fuzzy Sets and Systems - Theme: Learning and modeling
Modeling of hierarchical fuzzy systems
Fuzzy Sets and Systems - Theme: Learning and modeling
Novel Self-Organizing Takagi Sugeno Kang Fuzzy Neural Networks Based on ART-like Clustering
Neural Processing Letters
A Bayesian Approach to Joint Feature Selection and Classifier Design
IEEE Transactions on Pattern Analysis and Machine Intelligence
Biclustering Algorithms for Biological Data Analysis: A Survey
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Hierarchical TS fuzzy system and its universal approximation
Information Sciences—Informatics and Computer Science: An International Journal
Incremental Online Learning in High Dimensions
Neural Computation
Neural Networks - 2005 Special issue: IJCNN 2005
GA-TSKfnn: Parameters tuning of fuzzy neural network using genetic algorithms
Expert Systems with Applications: An International Journal
A class of hierarchical fuzzy systems with constraints on the fuzzy rules
IEEE Transactions on Fuzzy Systems
Ovarian cancer diagnosis with complementary learning fuzzy neural network
Artificial Intelligence in Medicine
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A computational intelligent system that models the human cognitive abilities may promise significant performance in problem learning because human is effective in learning and problem solving. Functionally modelling the human cognitive abilities not only avoids the details of the underlying neural mechanisms performing the tasks, but also reduces the complexity of the system. The complementary learning mechanism is responsible for human pattern recognition, i.e. human attends to positive and negative samples when making decision. Furthermore, human concept learning is organized in a hierarchical fashion. Such hierarchical organization allows the divide-and-conquer approach to the problem. Thus, integrating the functional models of hierarchical organization and complementary learning can potentially improve the performance in pattern recognition. Hierarchical complementary learning exhibits many of the desirable features of pattern recognition. It is further supported by the experimental results that verify the rationale of the integration and that the hierarchical complementary learning system is a promising pattern recognition tool.