A bootstrap technique for nearest neighbor classifier design
نویسندگان
چکیده
منابع مشابه
A Bootstrap Technique for Nearest Neighbor Classifier Design
A bootstrap technique for nearest neighbor classifier design is proposed. Our primary interest in designing a classifier is in small training sample size situations. Conventional bootstrapping techniques sample the training samples with replacement. On the other hand, our technique generates bootstrap samples by locally combining original training samples. The nearest neighbor classifier is des...
متن کاملa lattice based nearest neighbor classifier for anomaly intrusion detection
as networking and communication technology becomes more widespread, thequantity and impact of system attackers have been increased rapidly. themethodology of intrusion detection (ids) is generally classified into two broadcategories according to the detection approaches: misuse detection and anomalydetection. in misuse detection approach, abnormal system behavior is defined atfirst, and then an...
متن کاملA vector quantization method for nearest neighbor classifier design
This paper proposes a nearest neighbor classifier design method based on vector quantization (VQ). By investigating the error distribution pattern of the training set, the VQ technique is applied to generate prototypes incrementally until the desired classification result is reached. Experimental results demonstrate the effectiveness of the method. 2004 Elsevier B.V. All rights reserved.
متن کاملCenter-based nearest neighbor classifier
In this paper, a novel center-based nearest neighbor (CNN) classifier is proposed to deal with the pattern classification problems. Unlike nearest feature line (NFL) method, CNN considers the line passing through a sample point with known label and the center of the sample class. This line is called the center-based line (CL). These lines seem to have more capacity of representation for sample ...
متن کاملIterative improvement of a nearest neighbor classifier
In practical pattern recognition applications, the nearest neighbor classifier (NNC) is often applied because it does not require an a priori knowledge of the joint probability density of the input feature vectors. As the number of example vectors is increased, the error probability of the NNC approaches that of the Baysian classifier. However, at the same time, the computational complexity of ...
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 Pattern Analysis and Machine Intelligence
سال: 1997
ISSN: 0162-8828
DOI: 10.1109/34.566814