Results 1 
7 of
7
Statistical pattern recognition: A review
 IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
, 2000
"... The primary goal of pattern recognition is supervised or unsupervised classification. Among the various frameworks in which pattern recognition has been traditionally formulated, the statistical approach has been most intensively studied and used in practice. More recently, neural network techniques ..."
Abstract

Cited by 1035 (30 self)
 Add to MetaCart
The primary goal of pattern recognition is supervised or unsupervised classification. Among the various frameworks in which pattern recognition has been traditionally formulated, the statistical approach has been most intensively studied and used in practice. More recently, neural network techniques and methods imported from statistical learning theory have bean receiving increasing attention. The design of a recognition system requires careful attention to the following issues: definition of pattern classes, sensing environment, pattern representation, feature extraction and selection, cluster analysis, classifier design and learning, selection of training and test samples, and performance evaluation. In spite of almost 50 years of research and development in this field, the general problem of recognizing complex patterns with arbitrary orientation, location, and scale remains unsolved. New and emerging applications, such as data mining, web searching, retrieval of multimedia data, face recognition, and cursive handwriting recognition, require robust and efficient pattern recognition techniques. The objective of this review paper is to summarize and compare some of the wellknown methods used in various stages of a pattern recognition system and identify research topics and applications which are at the forefront of this exciting and challenging field.
Analysis of a Complexity Based Pruning Scheme for Classification Trees
 IEEE Transactions on Information Theory
, 2000
"... A complexity based pruning procedure for classification trees is described, and bounds on its finite sample performance are established. The procedure selects a subtree of a (possibly random) initial tree in order to minimize a complexity penalized measure of empirical risk. The complexity assigned ..."
Abstract

Cited by 9 (0 self)
 Add to MetaCart
(Show Context)
A complexity based pruning procedure for classification trees is described, and bounds on its finite sample performance are established. The procedure selects a subtree of a (possibly random) initial tree in order to minimize a complexity penalized measure of empirical risk. The complexity assigned to a subtree is proportional to the square root of its size. Two cases are considered. In the first the growing and pruning data sets are indentical, and in the second they are independent. Using the performance bound, the Bayes risk consistency of pruned trees obtained via the procedure is established when the sequence of initial trees satis es suitable geometric and structural constraints. The pruning method and its analysis are motivated by work on adaptive model selection using complexity regularization.
Analyzing And Improving Statistical Language Models For Speech Recognition
, 1994
"... A speech recognizer is a device that translates speech into text. Many current speech recognizers contain two components, an acoustic model and a statistical language model. The acoustic model indicates how likely it is that a certain word corresponds to a part of the acoustic signal (e.g. the speec ..."
Abstract

Cited by 5 (0 self)
 Add to MetaCart
(Show Context)
A speech recognizer is a device that translates speech into text. Many current speech recognizers contain two components, an acoustic model and a statistical language model. The acoustic model indicates how likely it is that a certain word corresponds to a part of the acoustic signal (e.g. the speech). The statistical language model indicates how likely it is that a certain word will be spoken next, given the words recognized so far. Even though the acoustic model might for example not be able to decide between the acoustically similar words "peach" and "teach", the statistical language model can indicate that the word "peach" is more likely if the previously recognized words are "He ate the". Current speech recognizers perform well on constrained tasks, but the goal of continuous, speaker independent speech recognition in potentially noisy environments with a very large vocabulary has not been reached so far. How can statistical language models be improved so that more complex tasks c...
Automatic Design of Binary Decision Trees Based on Genetic Programming
 Proc. The Second AsiaPacific Conference on Simulated Evolution and Learning (SEAL'98
, 1998
"... It is known that binary decision trees (BDTs) are very efficient for pattern recognition. If we design a BDT first, and then map it into a neural network, the network so obtained will have far fewer connections than that designed directly by using conventional (say, BP) learning algorithms. This is ..."
Abstract

Cited by 4 (2 self)
 Add to MetaCart
It is known that binary decision trees (BDTs) are very efficient for pattern recognition. If we design a BDT first, and then map it into a neural network, the network so obtained will have far fewer connections than that designed directly by using conventional (say, BP) learning algorithms. This is because, during the design of a BDT, useful features can be selected automatically, and be used in an efficient way. In practice, however, it is not so easy to design a neural network based on BDT, because to obtain an optimal BDT is an NPcomplete problem. In this paper, we apply the genetic programming (GP) to design of BDTs. To make the discussion more concrete, we will focus our discussion on a character recognition problem. The efficiency of the GP is verified through experimental results.
Decision Trees For Classification: A Review And Some New Results
"... Introduction Topdown induction of decision trees is a simple and powerful method of inferring classication rules from a set of labeled examples 1 . Each node of the tree implements a decision rule that splits the examples into two or more partitions. New nodes are created to handle each of the p ..."
Abstract

Cited by 4 (0 self)
 Add to MetaCart
Introduction Topdown induction of decision trees is a simple and powerful method of inferring classication rules from a set of labeled examples 1 . Each node of the tree implements a decision rule that splits the examples into two or more partitions. New nodes are created to handle each of the partitions and a node is considered terminal or a leaf node based on a stopping criteria. This standard approach to decision tree construction thus corresponds to a topdown greedy algorithm that makes locally optimal decisions at each node. There are two advantages that decision trees have over many other methods of classication methods. The rst is that the sequence of decisions made from the root node to the eventual labeling of a test input is easy to follow. This gives them an intuitive appeal that other methods of classication such as
A TradeOff Between Depth and Impurity for Pruning Decision Trees
"... Most pruning methods for decision trees minimize a classification error rate. In uncertain domains, some subtrees which do not lessen the error rate can be relevant to point out some populations of specific interest or to give a representation of a large data file. We propose here a new pruning met ..."
Abstract
 Add to MetaCart
Most pruning methods for decision trees minimize a classification error rate. In uncertain domains, some subtrees which do not lessen the error rate can be relevant to point out some populations of specific interest or to give a representation of a large data file. We propose here a new pruning method (called¢¤ £ pruning) which takes into account the complexity of subtrees and which is able to keep subtrees with leaves yielding to determinate relevant decision rules, even when keeping these ones does not increase the classification efficiency. 1