Topic Areas


NEURAL NETWORK MODELS

        Feedforward neural networks

        Recurrent neural networks

        Self-organizing maps

        Radial basis function networks

        Attractor neural networks and associative memory

        Modular networks

        Fuzzy neural networks

        Spiking neural networks

        Reservoir networks (echo-state networks, liquid-state machines, etc.)

        Large-scale neural networks

        Other topics in artificial neural networks

FUZZY SETS AND SYSTEMS

        Mathematical and theoretical foundations of fuzzy sets, fuzzy measures, and fuzzy integrals

        Interpretable and Interactive approaches to uncertainty in AI

        Fuzzy data analysis, fuzzy clustering, classification and pattern recognition

        Type-2 fuzzy sets, computing with words, and granular computing

        Fuzzy systems design and optimization

        Applications of fuzzy sets and systems, fuzzy measures and integrals

        Fuzzy and uncertain information processing, information extraction, and fusion

        Theory and applications of imprecise probabilities and possibilities

        Neuro- and evolutionary-fuzzy systems

EVOLUTIONARY COMPUTATION

        Ant colony optimization

        Particle swarm optimization

        Genetic algorithms

        Differential evolution

        Parallel and distributed algorithms

        Meta-modeling and surrogate models

        Evolutionary simulation-based optimization

        Discrete and combinatorial optimization

        Multi-objective evolutionary algorithms

        Evolutionary computation theory

        Evolutionary programming

        Evolved neural networks

        Evolutionary fuzzy systems

        Evolved neuro-fuzzy systems

MACHINE LEARNING

        Unsupervised learning and clustering, (including PCA, and ICA)

        Reinforcement learning

        Probabilistic and information-theoretic methods

        Support vector machines and kernel methods

        EM algorithms

        Mixture models, ensemble learning, and other meta-learning or committee algorithms

        Bayesian, belief, causal, and semantic networks

        Statistical and pattern recognition algorithms

        Visualization of data

        Feature selection, extraction, and aggregation

        Hybrid learning methods

        Deep learning

        Other topics in machine learning



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