منابع مشابه
Multiple-prototype classifier design
Five methods that generate multiple prototypes from labeled data are reviewed. Then we introduce a new sixth approach, which is a modification of Chang’s method. We compare the six methods with two standard classifier designs: the 1nearest prototype (1-np) and 1-nearest neighbor (1-nn) rules. The standard of comparison is the resubstitution error rate; the data used are the Iris data. Our modif...
متن کاملPrototype Classifier Design with Pruning
Algorithms reducing the storage requirement of the nearest neighbor classifier (NNC) can be divided into three main categories: Fast searching algorithms, Instance-based learning algorithms and Prototype based algorithms. In this paper an algorithm, called LVQPRU, is proposed for pruning NNC prototype vectors so that a compact classifier with good performance can be obtained. The basic condensi...
متن کاملDifferential Evolution Based Multiple Vector Prototype Classifier
In this article we introduce differential evolution based multiple vector prototype classifier (shortly MVDE). In this method we extend the previous DE classifier so that it can handle several class vectors in one class. Classification problems which are so complex that they are simply not separable by using distance based algorithms e.g. differential evolution (DE) classifier or support vector...
متن کاملPresupervised and post-supervised prototype classifier design
We extend the nearest prototype classifier to a generalized nearest prototype classifier (GNPC). The GNPC uses "soft" labeling of the prototypes in the classes, thereby encompassing a variety of classifiers. Based on how the prototypes are found we distinguish between presupervised and postsupervised GNPC designs. We derive the conditions for optimality (relative to the standard Bayes error rat...
متن کاملPrototype Based Classifier Design with Pruning
An algorithm is proposed to prune the prototype vectors (prototype selection) used in a nearest neighbor classifier so that a compact classifier can be obtained with similar or even better performance. The pruning procedure is error based; a prototype will be pruned if its deletion leads to the smallest classification error increase. Also each pruning iteration is followed by one epoch of Learn...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: IEEE Transactions on Systems, Man and Cybernetics, Part C (Applications and Reviews)
سال: 1998
ISSN: 1094-6977
DOI: 10.1109/5326.661091