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ICML
2008
IEEE
14 years 8 months ago
Sequence kernels for predicting protein essentiality
The problem of identifying the minimal gene set required to sustain life is of crucial importance in understanding cellular mechanisms and designing therapeutic drugs. This work d...
Cyril Allauzen, Mehryar Mohri, Ameet Talwalkar
ICML
2008
IEEE
14 years 8 months ago
Discriminative parameter learning for Bayesian networks
Bayesian network classifiers have been widely used for classification problems. Given a fixed Bayesian network structure, parameters learning can take two different approaches: ge...
Jiang Su, Harry Zhang, Charles X. Ling, Stan Matwi...
ICML
2008
IEEE
14 years 8 months ago
Stability of transductive regression algorithms
Corinna Cortes, Mehryar Mohri, Dmitry Pechyony, As...
ICML
2008
IEEE
14 years 8 months ago
Fast incremental proximity search in large graphs
In this paper we investigate two aspects of ranking problems on large graphs. First, we augment the deterministic pruning algorithm in Sarkar and Moore (2007) with sampling techni...
Purnamrita Sarkar, Andrew W. Moore, Amit Prakash
ICML
2008
IEEE
14 years 8 months ago
Estimating local optimums in EM algorithm over Gaussian mixture model
EM algorithm is a very popular iteration-based method to estimate the parameters of Gaussian Mixture Model from a large observation set. However, in most cases, EM algorithm is no...
Zhenjie Zhang, Bing Tian Dai, Anthony K. H. Tung
ICML
2008
IEEE
14 years 8 months ago
Bayesian multiple instance learning: automatic feature selection and inductive transfer
Vikas C. Raykar, Balaji Krishnapuram, Jinbo Bi, Mu...
ICML
2008
IEEE
14 years 8 months ago
Learning to classify with missing and corrupted features
After a classifier is trained using a machine learning algorithm and put to use in a real world system, it often faces noise which did not appear in the training data. Particularl...
Ofer Dekel, Ohad Shamir
ICML
2008
IEEE
14 years 8 months ago
Localized multiple kernel learning
Recently, instead of selecting a single kernel, multiple kernel learning (MKL) has been proposed which uses a convex combination of kernels, where the weight of each kernel is opt...
Ethem Alpaydin, Mehmet Gönen
ICML
2008
IEEE
14 years 8 months ago
No-regret learning in convex games
Quite a bit is known about minimizing different kinds of regret in experts problems, and how these regret types relate to types of equilibria in the multiagent setting of repeated...
Geoffrey J. Gordon, Amy R. Greenwald, Casey Marks