The article describes a method combining two widely-used empirical approaches to learning from examples: rule induction and instance-based learning. In our algorithm (RIONA) decision is predicted not on the basis of the whole support set of all rules matching a test case, but the support set restricted to a neighbourhood of a test case. The size of the optimal neighbourhood is automatically ind...

Hoppeld networks are commonly trained by one of two algorithms. The simplest of these is the Hebb rule, which has a low absolute capacity of n=(2 ln n), where n is the total number of neurons. This capacity can be increased to n by using the pseudo-inverse rule. However, capacity is not the only consideration. It is important for rules to be local (the weight of a synapse depends ony on informa...

The R-rule is a heuristic algorithm for distancebased neural network (DBNN) learning. Experimental results show that the R-rule can obtain the smallest or nearly smallest DBNNs. However, the computational cost of the R-rule is relatively high because the learning vector quantization (LVQ) algorithm is used iteratively during learning. To reduce the cost of the R-rule, we investigate three appro...

Based on an earlier study on lazy Bayesian rule learning, this paper introduces a general lazy learning framework, called LazyRule, that begins to learn a rule only when classifying a test case. The objective of the framework is to improve the performance of a base learning algorithm. It has the potential to be used for diierent types of base learning algorithms. LazyRule performs attribute eli...

In this paper we propose a method to implement in FPGA circuits, a feedforward neural network with on-chip delta rule learning algorithm. The method implies the building of a neural network by generic blocks designed in Mathworks’ Simulink environment. The main characteristics of this solution are on-chip learning algorithm implementation and high reconfiguration capability and operation under ...

Based on an earlier study on lazy Bayesian rule learning, this paper introduces a general lazy learning framework, called LazyRule, that begins to learn a rule only when classifying a test case. The objective of the framework is to improve the performance of a base learning algorithm. It has the potential to be used for diierent types of base learning algorithms. LazyRule performs attribute eli...

The requirements of real-world data mining problems vary extensively. It is plausible to assume that some of these requirements can be expressed as application-specific performance metrics. An algorithm that is designed to maximize performance given a certain learning metric may not produce the best possible result according to these applicationspecific metrics. We have implemented A Metric-bas...