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Article
Open AccessStructure sensitive complexity for symbol-free sequences
The study proposes our extended method to assess structure complexity for symbol-free sequences, such as literal texts, DNA sequences, rhythm, and musical input. This method is based on L-system and topologica...
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Chapter and Conference Paper
Autoencoder for Polysemous Word
Instead of training a single code vector for a word by using Elman network [1], this work presents a method to train multi-code for the polysemous word where each code represents a different meaning of the wor...
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Chapter and Conference Paper
About Eigenvalues from Embedding Data Complex in Low Dimension
LLE(Local linear embedding) and Isomap are widely used approaches for dimension reduction on data complex. The embedding results from the two methods are eigenvectors from solving specific matrices. The corres...
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Chapter and Conference Paper
Neighborhood Selection and Eigenvalues for Embedding Data Complex in Low Dimension
LLE(Local linear embedding) and Isomap are widely used approaches for dimension reduction. For LLE, the neighborhood selection approach is an important research issue. For different types of datasets, we need ...
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Chapter and Conference Paper
Self-Organizing Reinforcement Learning Model
A motor control model based on reinforcement learning (RL) is proposed here. The model is inspired by organizational principles of the cerebral cortex, specifically on cortical maps and functional hierarchy in...
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Chapter and Conference Paper
Intensity Gradient Self-organizing Map for Cerebral Cortex Reconstruction
This paper presents an application of a self-organizing map (SOM) model based on the image intensity gradient for the reconstruction of cerebral cortex from MR images. The cerebral cortex reconstruction is imp...
