AIMC Topic:
Learning

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Learning from adaptive neural dynamic surface control of strict-feedback systems.

IEEE transactions on neural networks and learning systems Jul 22, 2014
Learning plays an essential role in autonomous control systems. However, how to achieve learning in the nonstationary environment for nonlinear systems is a challenging problem. In this paper, we present learning method for a class of n th-order stri...
Artificial Intelligence Algorithms Computer Simulation Humans Neural Networks, Computer Learning Feedback Nonlinear Dynamics
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Is extreme learning machine feasible? A theoretical assessment (part I).

IEEE transactions on neural networks and learning systems Jul 21, 2014
An extreme learning machine (ELM) is a feedforward neural network (FNN) like learning system whose connections with output neurons are adjustable, while the connections with and within hidden neurons are randomly fixed. Numerous applications have dem...
Learning Models, Neurological Neurons Nerve Net Generalization, Psychological Artificial Intelligence Algorithms Humans Computer Simulation Neural Networks, Computer
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Learning to track multiple targets.

IEEE transactions on neural networks and learning systems Jul 14, 2014
Monocular multiple-object tracking is a fundamental yet under-addressed computer vision problem. In this paper, we propose a novel learning framework for tracking multiple objects by detection. First, instead of heuristically defining a tracking algo...
Humans Photic Stimulation Neural Networks, Computer Online Systems Markov Chains Signal Detection, Psychological Algorithms Mental Recall Learning Video Recording Pattern Recognition, Automated Models, Theoretical
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Upper limb robot-assisted therapy in cerebral palsy: a single-blind randomized controlled trial.

Neurorehabilitation and neural repair Jul 11, 2014
BACKGROUND: Several pilot studies have evoked interest in robot-assisted therapy (RAT) in children with cerebral palsy (CP).
Cerebral Palsy Upper Extremity Disability Evaluation Single-Blind Method Learning Psychomotor Performance Humans Biomechanical Phenomena Treatment Outcome Child Robotics
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Robust sensorimotor representation to physical interaction changes in humanoid motion learning.

IEEE transactions on neural networks and learning systems Jul 10, 2014
This paper proposes a learning from demonstration system based on a motion feature, called phase transfer sequence. The system aims to synthesize the knowledge on humanoid whole body motions learned during teacher-supported interactions, and apply th...
Functional Laterality Knowledge Motor Activity Symbolism Transfer, Psychology Robotics Walking Motion Learning Time Factors Humans Computer Simulation
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Combining extreme learning machines using support vector machines for breast tissue classification.

Computer methods in biomechanics and biomedical engineering Apr 29, 2013
In this paper, we present a new approach for breast tissue classification using the features derived from electrical impedance spectroscopy. This method is composed of a feature extraction method, feature selection phase and a classification step. Th...
Female Breast Neoplasms Support Vector Machine Humans Learning Breast Dielectric Spectroscopy
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Elucidating the Theoretical Underpinnings of Surrogate Gradient Learning in Spiking Neural Networks.

Neural computation Apr 17, 2025
Training spiking neural networks to approximate universal functions is essential for studying information processing in the brain and for neuromorphic computing. Yet the binary nature of spikes poses a challenge for direct gradient-based training. Su...
Humans Action Potentials Neurons Neural Networks, Computer Stochastic Processes Learning Models, Neurological
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g-Distance: On the comparison of model and human heterogeneity.

Psychological review Apr 1, 2025
Models are often evaluated when their behavior is at its closest to a single, sometimes averaged, set of empirical results, but this evaluation neglects the fact that both model and human behavior can be heterogeneous. Here, we develop a measure, -di...
Attention Models, Psychological Neural Networks, Computer Learning Humans
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Learning in Wilson-Cowan Model for Metapopulation.

Neural computation Mar 18, 2025
The Wilson-Cowan model for metapopulation, a neural mass network model, treats different subcortical regions of the brain as connected nodes, with connections representing various types of structural, functional, or effective neuronal connectivity be...
Humans Neural Networks, Computer Algorithms Models, Neurological Neurons Nerve Net Brain Learning
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A Layered Learning Approach to Scaling in Learning Classifier Systems for Boolean Problems.

Evolutionary computation Mar 15, 2025
Evolutionary Computation (EC) often throws away learned knowledge as it is reset for each new problem addressed. Conversely, humans can learn from small-scale problems, retain this knowledge (plus functionality), and then successfully reuse them in l...
Humans Machine Learning Algorithms Learning Problem Solving
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