The Denclue algorithm employs a cluster model based on kernel density estimation. A cluster is defined by a local maximum of the estimated density function. Data points are assigned to clusters by hill climbing, i.e. points going to the same local maximum are put into the same cluster. A disadvantage of Denclue 1.0 is, that the used hill climbing may make unnecessary small steps in the beginnin...

C4.5 algorithm is the most widely used algorithm in the decision trees so far and obviously the most popular heuristic function is gain ratio. This heuristic function has a serious disadvantage – towards dealing with irrelevant featured data sources. The hill climbing is a machine learning technique used in searching. It has good searching mechanism. Considering the relationship between hill cl...

Binary descriptors of image patches provide processing speed advantages and require less storage than methods that encode the patch appearance with a vector of real numbers. We provide evidence that, despite its simplicity, a stochastic hill climbing descriptor construction process defeats recently proposed alternatives on a standard discriminative power benchmark. The method is easy to impleme...

First-order learning systems (e.g., FOlL, FOCL, FORTE) generally rely on hill-climbing heuristics in order to avoid the combinatorial explosion inherent in learning first-order concepts. However, hill-climbing leaves these systems vulnerable to local maxima and local plateaus. We present a method, called relational pathfinding, which has proven highly effective in escaping local maxima and cros...

First-order learning systems (e.g., FOIL, FOCL, FORTE) generally rely on hill-climbing heuristics in order to avoid the combinatorial explosion inherent in learning first-order concepts. However, hill-climbing leaves these systems vulnerable to local maxima and local plateaus. We present a method, called relational pathfinding, which has proven highly effective in escaping local maxima and cros...

A comparison is made of the behaviour of some evolutionary algorithms in time-varying adaptive recursive filter systems. Simulations show that an algorithm including random immigrants outperforms a more conventional algorithm using the breeder genetic algorithm as the mutation operator when the time variation is discontinuous, but neither algorithms performs well when the time variation is rapi...

This paper reports on the use of search techniques to help optimise a case-based reasoning (CBR) system for predicting software project effort. A major problem, common to ML techniques in general, has been dealing with large numbers of case features, some of which can hinder the prediction process. Unfortunately searching for the optimal feature subset is a combinatorial problem and therefore N...

............................................................................................................. iv Introduction ........................................................................................................ 1 Background ........................................................................................................ 6 Proposed Methodology ............................

This paper presents techniques to integrate boundary overlap into concept assignment using Plausible Reasoning. Heuristic search techniques such as Hill climbing and Genetic Algorithms are investigated. A new fitness measure appropriate for overlapping concept assignment is introduced. The new algorithms are compared to randomly generated results and the Genetic Algorithm is shown to be the bes...