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» A selective sampling approach to active feature selection
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CVPR
2009
IEEE
15 years 5 months ago
An Instance Selection Approach to Multiple Instance Learning
Multiple-instance Learning (MIL) is a new paradigm of supervised learning that deals with the classification of bags. Each bag is presented as a collection of instances from whi...
Zhouyu Fu (Australian National University), Antoni...
CVPR
2004
IEEE
15 years 6 days ago
Detecting Unusual Activity in Video
We present an unsupervised technique for detecting unusual activity in a large video set using many simple features. No complex activity models and no supervised feature selection...
Hua Zhong, Jianbo Shi, Mirkó Visontai
ECCV
2006
Springer
15 years 2 days ago
Sampling Strategies for Bag-of-Features Image Classification
Abstract. Bag-of-features representations have recently become popular for content based image classification owing to their simplicity and good performance. They evolved from text...
Eric Nowak, Frédéric Jurie, Bill Tri...
ICASSP
2011
IEEE
13 years 1 months ago
Exemplar-based Sparse Representation phone identification features
Exemplar-based techniques, such as k-nearest neighbors (kNNs) and Sparse Representations (SRs), can be used to model a test sample from a few training points in a dictionary set. ...
Tara N. Sainath, David Nahamoo, Bhuvana Ramabhadra...
E2EMON
2006
IEEE
14 years 4 months ago
Active Probing Approach for Fault Localization in Computer Networks
—Active probing is an active network monitoring technique that has potential for developing effective solutions for fault localization. In this paper we use active probing to pre...
Maitreya Natu, Adarshpal S. Sethi