Nearest-Neighbor Methods in Learning and Vision Theory and Practice

Regression and classification methods based on similarity of the input to stored examples have not been widely used in applications involving very large sets of high-dimensional data. Recent advances in computational geometry and machine learning, however, may alleviate the problems in using these m...

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Hlavní autori: Shakhnarovich, Gregory, Darrell, Trevor, Indyk, Piotr
Médium: E-kniha Kniha
Jazyk:English
Vydavateľské údaje: Cambridge, Mass MIT Press 2006
The MIT Press
Vydanie:1
Edícia:Neural Information Processing series
Predmet:
Algorithms
Algorithms > Congresses
Computer Science
Computing and Processing
Congresses
Data processing
Geometry
Geometry > Data processing > Congresses
Machine learning
Machine Learning & Neural Networks
Machine learning > Congresses
Nearest neighbor analysis (Statistics)
Nearest neighbor analysis (Statistics) > Congresses
ISBN:9780262195478, 026219547X
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Obsah:
  • Intro -- Contents -- Series Foreword -- Preface -- 1 Introduction -- I THEORY -- 2 Nearest-Neighbor Searching and Metric Space Dimensions -- 3 Locality-Sensitive Hashing Using Stable Distributions -- II APPLICATIONS: LEARNING -- 4 New Algorithms for Efficient High-Dimensional Nonparametric Classification -- 5 Approximate Nearest Neighbor Regression in Very High Dimensions -- 6 Learning Embeddings for Fast Approximate Nearest Neighbor Retrieval -- III APPLICATIONS: VISION -- 7 Parameter-Sensitive Hashing for Fast Pose Estimation -- 8 Contour Matching Using Approximate Earth Mover's Distance -- 9 Adaptive Mean Shift Based Clustering in High Dimensions -- 10 Object Recognition using Locality Sensitive Hashing of Shape Contexts -- Contributors -- Index