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ICML
2007
IEEE
10 years 4 months ago
Adaptive dimension reduction using discriminant analysis and K-means clustering
We combine linear discriminant analysis (LDA) and K-means clustering into a coherent framework to adaptively select the most discriminative subspace. We use K-means clustering to ...
Chris H. Q. Ding, Tao Li
ICML
2007
IEEE
10 years 4 months ago
Percentile optimization in uncertain Markov decision processes with application to efficient exploration
Markov decision processes are an effective tool in modeling decision-making in uncertain dynamic environments. Since the parameters of these models are typically estimated from da...
Erick Delage, Shie Mannor
ICML
2007
IEEE
10 years 4 months ago
An integrated approach to feature invention and model construction for drug activity prediction
We present a new machine learning approach for 3D-QSAR, the task of predicting binding affinities of molecules to target proteins based on 3D structure. Our approach predicts bind...
David Page, Jesse Davis, Soumya Ray, Vítor ...
ICML
2007
IEEE
10 years 4 months ago
Learning to compress images and videos
We present an intuitive scheme for lossy color-image compression: Use the color information from a few representative pixels to learn a model which predicts color on the rest of t...
Li Cheng, S. V. N. Vishwanathan
ICML
2007
IEEE
10 years 4 months ago
Direct convex relaxations of sparse SVM
Although support vector machines (SVMs) for binary classification give rise to a decision rule that only relies on a subset of the training data points (support vectors), it will ...
Antoni B. Chan, Nuno Vasconcelos, Gert R. G. Lanck...
ICML
2007
IEEE
10 years 4 months ago
Local similarity discriminant analysis
We propose a local, generative model for similarity-based classification. The method is applicable to the case that only pairwise similarities between samples are available. The c...
Luca Cazzanti, Maya R. Gupta
ICML
2007
IEEE
10 years 4 months ago
Learning to rank: from pairwise approach to listwise approach
The paper is concerned with learning to rank, which is to construct a model or a function for ranking objects. Learning to rank is useful for document retrieval, collaborative fil...
Zhe Cao, Tao Qin, Tie-Yan Liu, Ming-Feng Tsai, Han...
ICML
2007
IEEE
10 years 4 months ago
Cluster analysis of heterogeneous rank data
Cluster analysis of ranking data, which occurs in consumer questionnaires, voting forms or other inquiries of preferences, attempts to identify typical groups of rank choices. Emp...
Ludwig M. Busse, Peter Orbanz, Joachim M. Buhmann
ICML
2007
IEEE
10 years 4 months ago
Solving multiclass support vector machines with LaRank
Optimization algorithms for large margin multiclass recognizers are often too costly to handle ambitious problems with structured outputs and exponential numbers of classes. Optim...
Antoine Bordes, Jason Weston, Léon Bottou, ...
ICML
2007
IEEE
10 years 4 months ago
Discriminative learning for differing training and test distributions
We address classification problems for which the training instances are governed by a distribution that is allowed to differ arbitrarily from the test distribution--problems also ...
Michael Brückner, Steffen Bickel, Tobias Sche...
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