Nearest neighbor (NN) norms: nn pattern classification techniques

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IEEE Computer Society Press, 1991 - Computers - 447 pages

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Contents

An Introductory Survey
1
An Efficient BranchandBound Nearest Neighbour Classifier 373
7
unk
11
CoreContributions
32
Nonparametric Discrimination Small Sample Performance
40
A Generalized kNearest Neighbor Rule
64
Initial Innovations
85
The DistanceWeighted kNearestNeighbor Rule
92
Design of Composite Classifier Systems in Imperfectly Supervised Environments
251
Recognition
259
Editing and Error Correction Using the Concept of Mutual Nearest Neighbourhood
268
A Link Between kNearest Neighbor Rules and Knowledge Based Systems
276
Editing Experiments
285
Asymptotic Properties of Nearest Neighbor Rules Using Edited Data
299
Finding Prototypes for Nearest Neighbor Classifiers
313
An Algorithm for a Selective Nearest Neighbor Decision Rule
319

AUniversal KNearestNeighborProcedureinDiscrimination
101
Algorithmic Advancements
107
AFuzzy KNearestNeighbor Algorithm
114
A ReExamination of the DistanceWeighted kNearest Neighbor Classification Rule
120
Training Sets and a priori Probabilities with the Nearest Neighbour Method
128
Convergence of the Nearest Neighbor Rule
145
Error and Reject Tradeoff for Nearest Neighbor Decision Rules
160
An Intrinsic Dimensionality Estimator from NearNeighbor Information
182
Classification Error for a Very Large Number of Classes
195
The Nearest Neighbor and the Bayes Error Rates
215
Imperfectly Identified Inputs and Partially Exposed Problem Environments
235
On the Edited Nearest Neighbor Rule
329
Computational Concerns
339
A WorstCase Analysis of Nearest Neighbor Searching by Projection
347
Approximative Fast NearestNeighbor Recognition
358
A Fast k Nearest Neighbor Finding Algorithm Based on the Ordered Partition
367
Clustering Concepts
387
Shared Near Neighbor Maximal Spanning Trees for Cluster Analysis
406
Disaggregative Clustering Using the Concept of Mutual Nearest Neighborhood
419
Related Reviews
433
About the Author
447
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