Works matching IS 15324435 AND DT 2007 AND VI 8 AND IP 7
Results: 10
Multi-class Protein Classification Using Adaptive Codes.
- Published in:
- Journal of Machine Learning Research, 2007, v. 8, n. 7, p. 1557
- By:
- Publication type:
- Article
Handling Missing Values when Applying Classification Models.
- Published in:
- Journal of Machine Learning Research, 2007, v. 8, n. 7, p. 1625
- By:
- Publication type:
- Article
An Interior-Point Method for Large-Scale ℓ<sub>1</sub>-Regularized Logistic Regression.
- Published in:
- Journal of Machine Learning Research, 2007, v. 8, n. 7, p. 1519
- By:
- Publication type:
- Article
On the Effectiveness of Laplacian Normalization for Graph Semi-supervised Learning.
- Published in:
- Journal of Machine Learning Research, 2007, v. 8, n. 7, p. 1489
- By:
- Publication type:
- Article
Spherical-Homoscedastic Distributions: The Equivalency of Spherical and Normal Distributions in Classification.
- Published in:
- Journal of Machine Learning Research, 2007, v. 8, n. 7, p. 1583
- By:
- Publication type:
- Article
Attribute-Efficient and Non-adaptive Learning of Parities and DNF Expressions.
- Published in:
- Journal of Machine Learning Research, 2007, v. 8, n. 7, p. 1431
- By:
- Publication type:
- Article
Generalization Error Bounds in Semi-supervised Classification Under the Cluster Assumption.
- Published in:
- Journal of Machine Learning Research, 2007, v. 8, n. 7, p. 1369
- By:
- Publication type:
- Article
PAC-Bayes Risk Bounds for Stochastic Averages and Majority Votes of Sample-Compressed Classifiers.
- Published in:
- Journal of Machine Learning Research, 2007, v. 8, n. 7, p. 1461
- By:
- Publication type:
- Article
Learning to Classify Ordinal Data: The Data Replication Method.
- Published in:
- Journal of Machine Learning Research, 2007, v. 8, n. 7, p. 1393
- By:
- Publication type:
- Article
Compression-Based Averaging of Selective Naive Bayes Classifiers.
- Published in:
- Journal of Machine Learning Research, 2007, v. 8, n. 7, p. 1659
- By:
- Publication type:
- Article