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NIPS
2008
15 years 3 months ago
Sparse Online Learning via Truncated Gradient
We propose a general method called truncated gradient to induce sparsity in the weights of onlinelearning algorithms with convex loss functions. This method has several essential ...
John Langford, Lihong Li, Tong Zhang
GECCO
2000
Springer
121views Optimization» more  GECCO 2000»
15 years 6 months ago
Metaphor for learning: an evolutionary algorithm
The organizational algorithm is examined as a computational approach to representing interpersonal learning. The structure of the algorithm is introduced and described in context ...
Jody Lee Louse, Alexander Kain, James Hines
147
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EMMCVPR
2011
Springer
14 years 2 months ago
Optimization of Robust Loss Functions for Weakly-Labeled Image Taxonomies: An ImageNet Case Study
The recently proposed ImageNet dataset consists of several million images, each annotated with a single object category. However, these annotations may be imperfect, in the sense t...
Julian John McAuley, Arnau Ramisa, Tibério ...
ECCV
2010
Springer
15 years 6 months ago
Learning a Fine Vocabulary
We present a novel similarity measure for bag-of-words type large scale image retrieval. The similarity function is learned in an unsupervised manner, requires no extra space over ...
AAAI
1998
15 years 3 months ago
Machine Learning of Generic and User-Focused Summarization
A key problem in text summarization is finding a salience function which determines what information in the source should be included in the summary. This paper describes the use ...
Inderjeet Mani, Eric Bloedorn