The symbolic machine learning techniques, the neural network approach, and genetic algorithms provide different methods of data analysis and knowledge discovery (Mooney, Shavlik, et al. 1989; Quinlan, J. 1994; Tank and Hopfield 1987; Craven and Shavlik 1993; Koza 1992). As these techniques have shown unique capabilities for analyzing symbolic and numeric data, we briefly survey underlying appro...

Predictions computed by a classification tree are usually constant on axis-parallel hyperrectangles corresponding to the leaves and have strict jumps on their boundaries. The density function of the underlying class distribution may be continuous and the gradient vector may not be parallel to any of the axes. In these cases a better approximation may be expected, if the prediction function of t...

The seminal work of Zadeh (1965), fuzzy set theory (FST) has developed into a methodology fundamental to analysis that incorporates vagueness and ambiguity. With respect to the area of data mining, it endeavours to find potentially meaningful patterns from data (Hu & Tzeng, 2003). This includes the construction of if-then decision rule systems, which attempt a level of inherent interpretability...

There are several diierent ways data mining (the automatic induction of knowledge from data) can be applied to the problem of natural language processing. In the past, data mining techniques have mainly been used in linguistic engineering applications to solve knowledge acquisition bottlenecks. In this paper, we show that they can also assist in linguistic theory formation by providing a new to...

Model trees, which are a type of decision tree with linear regression functions at the leaves, form the basis of a recent successful technique for predicting continuous numeric values. They can be applied to classiication problems by employing a standard method of transforming a classiication problem into a problem of function approximation. Surprisingly, using this simple transformation the mo...

Typically, decision tree construction algorithms apply a single \goodness of split" criterion to form each test node of the tree. It is a hypothesis of this research that better results can be obtained if during tree construction one applies a split criterion suited to the \location" of the test node in the tree. Speciically, given the objective of maximizing predictive accuracy, test nodes nea...

same bias may increase predictive accuracy if it is appropriately applied. Section 4.2 suggests , for example, that a bias toward small trees is relatively more appropriate when attribute errors play an important role in data generation. These details of the factors that determine when cross-validation pruning will increase or decrease predictive accuracy are less crucial for the machine learni...

Data mining is the useful tool to discovering the knowledge from large data. Different methods & algorithms are available in data mining. Classification is most common method used for finding the mine rule from the large database. Decision tree method generally used for the Classification, because it is the simple hierarchical structure for the user understanding & decision making. Various data...

Dependency and self-criticism are vulnerability factors for depression. How these personality factors change with treatment for depression and how they relate to symptom change across different types of treatment require further research. In addition, cultural differences that interact with the dependency/self-criticism-depression relation remain underinvestigated. We randomly assigned 149 adul...

ALESSI, N. E. AND P. QUINLAN. Postnatal development of ACTH and a-MSH in the medulla oblongata of rat: a-MSH i.s the predominant peptide. PE PTIDES 6: Suppl. 2, 137-141, 1985.-ACTH and a-MSH levels were measured by radioimmunoassays in extracts of the caudal medulla oblongata of developing rats on postnatal (P) days 1-42 at 7 day intervals, and in adult rats. From PI to adulthood, ACTH increase...