Richness of the Base and Probabilistic Unsupervised Learning in Optimality Theory

INTEGRATED ADAPTIVE FUZZY CLUSTERING (IAFC) NEURAL NETWORKS USING FUZZY LEARNING RULES

The proposed IAFC neural networks have both stability and plasticity because theyuse a control structure similar to that of the ART-1(Adaptive Resonance Theory) neural network.The unsupervised IAFC neural network is the unsupervised neural network which uses the fuzzyleaky learning rule. This fuzzy leaky learning rule controls the updating amounts by fuzzymembership values. The supervised IAFC ...

Optimality Theoretic Account of Acquisition of Consonant Clusters of English Syllables by Persian EFL Learners*

This study accounts for the acquisition of the consonant clusters of English syllable structures both in onset and coda positions by Persian EFL learners. Persian syllable structure is "CV(CC)", composed of one consonant at the initial position and two optional consonants at the final position; whereas English syllable structure is "(CCC)V(CCCC)". Therefore, Persian EFL learners need to resolve...

High-Dimensional Unsupervised Active Learning Method

In this work, a hierarchical ensemble of projected clustering algorithm for high-dimensional data is proposed. The basic concept of the algorithm is based on the active learning method (ALM) which is a fuzzy learning scheme, inspired by some behavioral features of human brain functionality. High-dimensional unsupervised active learning method (HUALM) is a clustering algorithm which blurs the da...

Optimality Of Monetary And Fiscal Policies In Iran: An Application Of The Stochastic Optimal Control Theory

The Initial State and 'Richness of the Base' in Optimality Theory