We describe a novel approach for fully automated face recognition and show its feasibility on a large data base of facial images (FERET). Our approach, based on a hybrid architecture consisting of an ensemble of connectionist networks radial basis functions (RBF) and inductive decision trees (DT), combines the merits of ’discrete and abstractive’ features with those of ’holistic’ template match...

Entropy Guided Transformation Learning (ETL) is a new machine learning strategy that combines the advantages of decision trees (DT) and Transformation Based Learning (TBL). In this work, we apply the ETL framework to four phrase chunking tasks: Portuguese noun phrase chunking, English base noun phrase chunking, English text chunking and Hindi text chunking. In all four tasks, ETL shows better r...

This paper considers hybrid classification architectures and shows their feasibility on large data bases consisting of facial images. Our architecture, consists of an ensemble of connectionist networks radial basis functions (RBF) and decision trees (DT). This architecture enjoys robustness via (i) consensus provided by ensembles of RBF networks, and (ii) categorical classification using decisi...

The paper proposes a solution for the problem of optimizing medium voltage power systems which supply, among others, nonlinear loads. It is focused on decision tree (DT) application sizing and allocation active filters (APFs), are most effective means quality improvement. Propositions some DT strategies followed by results have been described in paper. On basis an example medium-voltage network...

In this paper, we propose a piecewise linear decision tree and its generalized form, namely the (G)PWL-DT, which introduces linearity overcomes discontinuity of existing constant trees (PWC-DT). The proposed (G)PWL-DT inherits basic topology interpretability by recursively partitioning domain into subregions, are represented leaf nodes. Rather than indicator function, employs rectifier units (R...

We analyzed the hepatitis data by Decision Tree GraphBased Induction (DT-GBI), which constructs a decision tree for graphstructured data while simultaneously constructing attributes for classification. An attribute at each node in the decision tree is a discriminative pattern (subgraph) in the input graph, and extracted by Graph-Based Induction (GBI). We conducted four kinds of experiments usin...

In this paper, a new decision tree-based clustering technique called Phonetic, Dimensional and State Positional Decision Tree (PDS-DT) is proposed. In PDS-DT, phonetic contexts, dimensions and state positions are grouped simultaneously during decision tree construction. PDS-DT provides a complicate distribution sharing structure without any external control parameters. In speaker-independent co...

decision trees as one of the data mining techniques, is used in credit scoring of bank customers. the main problem is the construction of decision trees in that they can classify customers optimally. this paper proposes an appropriate model based on genetic algorithm for credit scoring of banks customers in order to offer credit facilities to each class. genetic algorithm can help in credit sco...

From the parsing point of view, the derivation tree in TAG [hereafter DT] is seen as the "history" of the derivation but also as a linguistic representation, closer to semantics, that can be the basis of a further analysis. Because in TAG the elementary trees are lexicalized and localize the predicate-arguments relations, several works have compared the DT to a structure involving dependencies ...

A machine learning technique called Graph-Based Induction (GBI) efficiently extracts typical patterns from graph-structured data by stepwise pair expansion (pairwise chunking). It is very efficient because of its greedy search. Meanwhile, a decision tree is an effective means of data classification from which rules that are easy to understand can be obtained. However, a decision tree could not ...