This report is a complement to the working document [4], where a sparse associative network is described. This report shows that the net learning rule in [4] can be viewed as the solution to a weighted least squares problem. This means that we can apply the theory framework of least squares problems, and compare the net rule with some other iterative algorithms that solve the same problem. The ...

Aiming at value reduction, a sort of RSVR algorithm was presented based on support in association rules via Apriori algorithm. A more effective reduction table can be obtained by deleting those rules with less support according to least support— minsup. The reduction feasibility of this algorithm was achieved by reducing the given decision table. Testing by UCI machine learning database and com...

Associative classification (AC) which is based on association rules has shown great promise over many other classification techniques on static dataset. Meanwhile, a new challenge have been proposed in that the increasing prominence of data streams arising in a wide range of advanced application. This paper describes and evaluates a new associative classification algorithm for data streams AC-D...

A new learning rule (Precise-Spike-Driven (PSD) Synaptic Plasticity) is proposed for processing and memorizing spatiotemporal patterns. PSD is a supervised learning rule that is analytically derived from the traditional Widrow-Hoff rule and can be used to train neurons to associate an input spatiotemporal spike pattern with a desired spike train. Synaptic adaptation is driven by the error betwe...

Since the publication of the PDP volumes in 1986,1 learning by backpropagation has become the most popular method of training neural networks. The reason for the popularity is the underlying simplicity and relative power of the algorithm. Its power derives from the fact that, unlike its precursors, the perceptron learning rule and the Widrow-Hoff learning rule, it can be employed for training n...

This paper analyses the complexity of rule selection for supervised learning in distributed scenarios. The selection of rules is usually guided by a utility measure such as predictive accuracy or weighted relative accuracy. Other examples are support and confidence, known from association rule mining. A common strategy to tackle rule selection from distributed data is to evaluate rules locally ...

This paper introduces a multistrategy learning approach to the categorization of text documents. The approach benefits from two existing, and in our view complimentary, sets of categorization techniques: those based on Rocchio’s algorithm and those belonging to the rule learning class of machine learning algorithms. Visualization is used for the presentation of the output of learning.

Learning classifier system is a machine learning technique which combines genetic algorithm with the power of the reinforcement learning paradigm. This rule based system has been inspired by the general principle of Darwinian evolution and cognitive learning. XCS, eXtended Classifier System, is currently considered as state-of-the-art learning classifier systems due to its effectiveness in data...

The work presented in this paper is an implementation of the Q-learning algorithm to optimise the rule execution process of Judicial Advisory Expert System (JAES). JAES covers the Sale of Goods Act (SGA) sections 16 to 20, UK. Addition of further sections to JAES led to its performance degradation; hence adopting an algorithm which improves the rule execution process was necessary. The existing...

This study proposes a modified version of cultural algorithms (CAs) which benefits from rule-based system for influence function. This rule-based system selects and applies the suitable knowledge source according to the distribution of the solutions. This is important to use appropriate influence function to apply to a specific individual, regarding to its role in the search process. This rule ...