Due to the vast and rapid increase in data, data mining has become an increasingly important tool for the purpose of knowledge discovery in order to prevent the presence of rich data but poor knowledge. Data mining tasks can be undertaken in two ways, namely, manual walkthrough of data and use of machine learning approaches. Due to the presence of big data, machine learning has thus become a po...

A computational model of sequence learning is described that is based on pairwise associations and generalization. Simulations by the model predicted that rats should learn a long monotonic pattern of food quantities better than a nonmonotonic pattern, as predicted by rule-learning theory, and that they should learn a short nonmonotonic pattern with highly discriminable elements better than 1 w...

Category rule learning was examined in two amnesic patients using the perceptual categorization task (e.g., Ashby & Gott, 1988; Filoteo & Maddox, 1999). Traditional accuracy-based analyses as well as quantitative model-based analyses were performed. Unlike accuracy-based analyses, the model-based approach allowed us to examine both categorization rule learning and variability in the trial-by-tr...

In this paper fuzzy rule inconsistency resolution and fuzzy rule insertion methods are proposed for fuzzy neural networks. Necessity support and possibility support (referred to as support pair) are applied to detect and remove inconsistencies. In addition to the support pair, the concept of initial learning point is used to handle rule insertion. We demonstrate the use of the proposed methods ...

We present a novel learning rule for learning transformations of sophisticated neural representations in a biologically plausible manner. We show that the rule, which uses only information available locally to a synapse in a spiking network, can learn to transmit and bind semantic pointers. Semantic pointers have previously been used to build Spaun, which is currently the world’s largest functi...

Neural network models offer a theoretical testbed for the study of learning at the cellular level. The only experimentally verified learning rule, Hebb’s rule, is extremely limited in its ability to train networks to perform complex tasks. An identified cellular mechanism responsible for Hebbian-type long-term potentiation, the NMDA receptor, is highly versatile. Its function and efficacy are m...

This paper presents to model the Hebb learning rule and proposes a neuron learning machine (NLM). Hebb learning rule describes the plasticity of the connection between presynaptic and postsynaptic neurons and it is unsupervised itself. It formulates the updating gradient of the connecting weight in artificial neural networks. In this paper, we construct an objective function via modeling the He...

Currently all important, low-level, unsupervised network learning algorithms follow the paradigm of Hebb, where input and output activity are correlated to change the connection strength of a synapse. However, as a consequence, classical Hebbian learning always carries a potentially destabilizing autocorrelation term, which is due to the fact that every input is in a weighted form reflected in ...

Currently all important, low-level, unsupervised network learning algorithms follow the paradigm of Hebb, where inputand output activity are correlated to change the connection strength of a synapse. However, as a consequence, classical Hebbian learning always carries a potentially destabilising autocorrelation term which is due to the fact that every input is in a weighted form reflected in th...

Most work on unsupervised entailment rule acquisition focused on rules between templates with two variables, ignoring unary rules entailment rules between templates with a single variable. In this paper we investigate two approaches for unsupervised learning of such rules and compare the proposed methods with a binary rule learning method. The results show that the learned unary rule-sets outpe...