A comparison of the reliability of three pattern recognition classifiers has been made using data having a great amount of variation. The basic concepts of the Linear Learning Machine, the K Nearest Neighbor Classifier, and the Potential Function Classifier are presented. Prediction of whether a student would pass or fail freshmen Chemistry 120 was made, based on various test results. The Linear Learning Machine was found to be an unworkable classifier for this kind of data. Both the Potential Function Classifier and the K Nearest Neighbor classifier were acceptable with the Potential Function Classifier being generally a better classifier
Abstract. Twenty-two decision tree, nine statistical, and two neural network algorithms are compared...
In recent years pattern recognition has evolved to a mature discipline and has been successfully app...
This paper is a survey of research on pattern classifier. In particular, it emphasizes on the differ...
Abstract. Pattern recognition techniques have been employed in a myriad of industrial, medical, comm...
Pattern recognition has been employed in a myriad of industrial, commercial and academic application...
In this study computer simulation is used to compare selected pattern recognition functions. The Hig...
The purpose of this study is to briefly learn the theory and implementation of three most commonly u...
Pattern recognition techniques are divided into categories of supervised, unsupervised and semi supe...
Closely related to the concept of Machine Learning, Pattern Recognition is the assignment of an outp...
Scope and Method of Study: An expansion and modification of the Bledsoe-Browning n-truple technique ...
In this paper, we review some pattern recognition schemes published in recent years. After giving th...
The classification of signals through the use of pattern recognition techniques may be viewed as a s...
Machine learning is a popular way to find patterns and relationships in high complex datasets. With ...
A novel method for evaluating the reliability of a classifier on a pattern is proposed based on the ...
. Twenty-two decision tree, nine statistical, and two neural network algorithms are compared on thir...
Abstract. Twenty-two decision tree, nine statistical, and two neural network algorithms are compared...
In recent years pattern recognition has evolved to a mature discipline and has been successfully app...
This paper is a survey of research on pattern classifier. In particular, it emphasizes on the differ...
Abstract. Pattern recognition techniques have been employed in a myriad of industrial, medical, comm...
Pattern recognition has been employed in a myriad of industrial, commercial and academic application...
In this study computer simulation is used to compare selected pattern recognition functions. The Hig...
The purpose of this study is to briefly learn the theory and implementation of three most commonly u...
Pattern recognition techniques are divided into categories of supervised, unsupervised and semi supe...
Closely related to the concept of Machine Learning, Pattern Recognition is the assignment of an outp...
Scope and Method of Study: An expansion and modification of the Bledsoe-Browning n-truple technique ...
In this paper, we review some pattern recognition schemes published in recent years. After giving th...
The classification of signals through the use of pattern recognition techniques may be viewed as a s...
Machine learning is a popular way to find patterns and relationships in high complex datasets. With ...
A novel method for evaluating the reliability of a classifier on a pattern is proposed based on the ...
. Twenty-two decision tree, nine statistical, and two neural network algorithms are compared on thir...
Abstract. Twenty-two decision tree, nine statistical, and two neural network algorithms are compared...
In recent years pattern recognition has evolved to a mature discipline and has been successfully app...
This paper is a survey of research on pattern classifier. In particular, it emphasizes on the differ...