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ECML
2006
Springer
9 years 5 months ago
Active Learning with Irrelevant Examples
Abstract. Active learning algorithms attempt to accelerate the learning process by requesting labels for the most informative items first. In real-world problems, however, there ma...
Dominic Mazzoni, Kiri Wagstaff, Michael C. Burl
CSFW
2004
IEEE
9 years 5 months ago
Using Active Learning in Intrusion Detection
Intrusion Detection Systems (IDSs) have become an important part of operational computer security. They are the last line of defense against malicious hackers and help detect ongo...
Magnus Almgren, Erland Jonsson
COLT
2007
Springer
9 years 7 months ago
Minimax Bounds for Active Learning
This paper analyzes the potential advantages and theoretical challenges of “active learning” algorithms. Active learning involves sequential sampling procedures that use infor...
Rui Castro, Robert D. Nowak
KDD
2008
ACM
207views Data Mining» more  KDD 2008»
10 years 1 months ago
Active learning with direct query construction
Active learning may hold the key for solving the data scarcity problem in supervised learning, i.e., the lack of labeled data. Indeed, labeling data is a costly process, yet an ac...
Charles X. Ling, Jun Du
ICML
2005
IEEE
10 years 2 months ago
Active learning for Hidden Markov Models: objective functions and algorithms
Hidden Markov Models (HMMs) model sequential data in many fields such as text/speech processing and biosignal analysis. Active learning algorithms learn faster and/or better by cl...
Brigham Anderson, Andrew Moore
ICML
2009
IEEE
10 years 2 months ago
Importance weighted active learning
We propose an importance weighting framework for actively labeling samples. This technique yields practical yet sound active learning algorithms for general loss functions. Experi...
Alina Beygelzimer, Sanjoy Dasgupta, John Langford
CVPR
2007
IEEE
10 years 3 months ago
Practical Online Active Learning for Classification
We compare the practical performance of several recently proposed algorithms for active learning in the online classification setting. We consider two active learning algorithms (...
Claire Monteleoni, Matti Kääriäinen
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