DMCA
Active Learning for Large Multi-class Problems
Cached
Download Links
| Citations: | 50 - 0 self |
BibTeX
@MISC{Jain_activelearning,
author = {Prateek Jain and Ashish Kapoor},
title = {Active Learning for Large Multi-class Problems},
year = {}
}
Share
 |
 |
 |
 |
||
OpenURL
Abstract
Scarcity and infeasibility of human supervision for large scale multi-class classification problems necessitates active learning. Unfortunately, existing active learning methods for multi-class problems are inherently binary methods and do not scale up to a large number of classes. In this paper, we introduce a probabilistic variant of the K-Nearest Neighbor method for classification that can be seamlessly used for active learning in multi-class scenarios. Given some labeled training data, our method learns an accurate metric/kernel function over the input space that can be used for classification and similarity search. Unlike existing metric/kernel learning methods, our scheme is highly scalable for classification problems and provides a natural notion of uncertainty over class labels. Further, we use this measure of uncertainty to actively sample training examples that maximize discriminating capabilities of the model. Experiments on benchmark datasets show that the proposed method learns appropriate distance metrics that lead to state-of-the-art performance for object categorization problems. Furthermore, our active learning method effectively samples training examples, resulting in significant accuracy gains over random sampling for multi-class problems involving a large number of classes. 1.
Keyphrases
active learning large multi-class problem large number active learning method multi-class problem natural notion probabilistic variant significant accuracy gain k-nearest neighbor method human supervision large scale multi-class classification problem classification problem labeled training data binary method appropriate distance metric benchmark datasets metric kernel class label multi-class scenario sample training example training example state-of-the-art performance input space similarity search accurate metric kernel function object categorization problem
