AI Medical Compendium Topic:
Learning

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Model-based reinforcement learning with dimension reduction.

Neural networks : the official journal of the International Neural Network Society
The goal of reinforcement learning is to learn an optimal policy which controls an agent to acquire the maximum cumulative reward. The model-based reinforcement learning approach learns a transition model of the environment from data, and then derive...

Statistical learning theory for high dimensional prediction: Application to criterion-keyed scale development.

Psychological methods
Statistical learning theory (SLT) is the statistical formulation of machine learning theory, a body of analytic methods common in "big data" problems. Regression-based SLT algorithms seek to maximize predictive accuracy for some outcome, given a larg...

A single robotic session that guides or increases movement error in survivors post-chronic stroke: which intervention is best to boost the learning of a timing task?

Disability and rehabilitation
PURPOSE: Timing deficits can have a negative impact on the lives of survivors post-chronic stroke. Studies evaluating ways to improve timing post stroke are scarce. The goal of the study was to evaluate the impact of a single session of haptic guidan...

The evaluation of i-SIDRA - a tool for intelligent feedback - in a course on the anatomy of the locomotor system.

International journal of medical informatics
OBJECTIVE: This paper presents an empirical study of a formative mobile-based assessment approach that can be used to provide students with intelligent diagnostic feedback to test its educational effectiveness.

Kernel Recursive Least-Squares Temporal Difference Algorithms with Sparsification and Regularization.

Computational intelligence and neuroscience
By combining with sparse kernel methods, least-squares temporal difference (LSTD) algorithms can construct the feature dictionary automatically and obtain a better generalization ability. However, the previous kernel-based LSTD algorithms do not cons...

Reaction-diffusion-like formalism for plastic neural networks reveals dissipative solitons at criticality.

Physical review. E
Self-organized structures in networks with spike-timing dependent synaptic plasticity (STDP) are likely to play a central role for information processing in the brain. In the present study we derive a reaction-diffusion-like formalism for plastic fee...

Deep Learning and Developmental Learning: Emergence of Fine-to-Coarse Conceptual Categories at Layers of Deep Belief Network.

Perception
In this paper, I investigate conceptual categories derived from developmental processing in a deep neural network. The similarity matrices of deep representation at each layer of neural network are computed and compared with their raw representation....

Evaluation of tactical training in team handball by means of artificial neural networks.

Journal of sports sciences
While tactical performance in competition has been analysed extensively, the assessment of training processes of tactical behaviour has rather been neglected in the literature. Therefore, the purpose of this study is to provide a methodology to asses...

A Fly-Inspired Mushroom Bodies Model for Sensory-Motor Control Through Sequence and Subsequence Learning.

International journal of neural systems
Classification and sequence learning are relevant capabilities used by living beings to extract complex information from the environment for behavioral control. The insect world is full of examples where the presentation time of specific stimuli shap...

A swarm-trained k-nearest prototypes adaptive classifier with automatic feature selection for interval data.

Neural networks : the official journal of the International Neural Network Society
Some complex data types are capable of modeling data variability and imprecision. These data types are studied in the symbolic data analysis field. One such data type is interval data, which represents ranges of values and is more versatile than clas...