Original Contribution: Parity with two layer feedforward nets
Neural Networks
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Computational Explorations in Cognitive Neuroscience: Understanding the Mind by Simulating the Brain
Computational Explorations in Cognitive Neuroscience: Understanding the Mind by Simulating the Brain
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
The Minimum Number of Errors in the N-Parity and its Solution with an Incremental Neural Network
Neural Processing Letters
A Solution for the N-bit Parity Problem Using a Single Translated Multiplicative Neuron
Neural Processing Letters
Nonlocal Estimation of Manifold Structure
Neural Computation
The Dissimilarity Representation for Pattern Recognition: Foundations And Applications (Machine Perception and Artificial Intelligence)
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Backpropagation applied to handwritten zip code recognition
Neural Computation
Solving multiclass learning problems via error-correcting output codes
Journal of Artificial Intelligence Research
Towards understanding of natural language: neurocognitive inspirations
ICANN'07 Proceedings of the 17th international conference on Artificial neural networks
ICANN'06 Proceedings of the 16th international conference on Artificial Neural Networks - Volume Part I
A new methodology of extraction, optimization and application of crisp and fuzzy logical rules
IEEE Transactions on Neural Networks
Uncertainty of data, fuzzy membership functions, and multilayer perceptrons
IEEE Transactions on Neural Networks
Autonomic and cognitive possibilities for information or neural-like systems using dynamic links
WSEAS TRANSACTIONS on SYSTEMS
Autonomic and cognitive possibilities for information or neural-like systems using dynamic links
WSEAS TRANSACTIONS on SYSTEMS
Characterization of Ag-PEO10LiCF3SO3-polypyrrol-Au neural switch
WSEAS Transactions on Computers
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Computational systems are still far behind biological systems in object recognition, reasoning or analysis of language structures. What kind of data structures can be learned from data with existing machine learning algorithms? Neurocognitive inspirations show why existing learning systems cannot compete with biological ones. They point the way to more efficient algorithms, generating simplest reliable models of data and capable of object recognition with undetermined number of features. The goal of learning in neural networks and other systems is to transform data into linearly separable data clusters. This is sufficient for relatively simple problems, but makes learning almost impossible if the logic inherent in data is complex. New non-separable targets for learning are introduced to simplify learning and to characterize non-separable problems into classes of growing complexity. Neurobiological and formal justification for new learning targets are given and the case of Boolean functions analyzed.