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COLT
2005
Springer
15 years 3 months ago
Analysis of Perceptron-Based Active Learning
We start by showing that in an active learning setting, the Perceptron algorithm needs Ω( 1 ε2 ) labels to learn linear separators within generalization error ε. We then prese...
Sanjoy Dasgupta, Adam Tauman Kalai, Claire Montele...
DAGM
2005
Springer
15 years 3 months ago
Rapid Online Learning of Objects in a Biologically Motivated Recognition Architecture
We present an approach for the supervised online learning of object representations based on a biologically motivated architecture of visual processing. We use the output of a rece...
Stephan Kirstein, Heiko Wersing, Edgar Körner
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IDA
2005
Springer
15 years 3 months ago
Learning from Ambiguously Labeled Examples
Inducing a classification function from a set of examples in the form of labeled instances is a standard problem in supervised machine learning. In this paper, we are concerned w...
Eyke Hüllermeier, Jürgen Beringer
AAAI
2008
15 years 13 days ago
Active Learning for Pipeline Models
For many machine learning solutions to complex applications, there are significant performance advantages to decomposing the overall task into several simpler sequential stages, c...
Dan Roth, Kevin Small
NAACL
2010
14 years 8 months ago
Constraint-Driven Rank-Based Learning for Information Extraction
Most learning algorithms for undirected graphical models require complete inference over at least one instance before parameter updates can be made. SampleRank is a rankbased lear...
Sameer Singh, Limin Yao, Sebastian Riedel, Andrew ...