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ML
2015
ACM
5 years 7 months ago
Direct conditional probability density estimation with sparse feature selection
Regression is a fundamental problem in statistical data analysis, which aims at estimating the conditional mean of output given input. However, regression is not informative enoug...
Motoki Shiga, Voot Tangkaratt, Masashi Sugiyama
ML
2015
ACM
5 years 7 months ago
Greedy learning of latent tree models for multidimensional clustering
Tengfei Liu, Nevin Lianwen Zhang, Peixian Chen, Ap...
ML
2015
ACM
5 years 7 months ago
Unconfused ultraconservative multiclass algorithms
We tackle the problem of learning linear classifiers from noisy datasets in a multiclass setting. The two-class version of this problem was studied a few years ago by, e.g. Bylan...
Ugo Louche, Liva Ralaivola
ML
2015
ACM
5 years 7 months ago
Unsupervised ensemble minority clustering
Cluster analysis lies at the core of most unsupervised learning tasks. However, the majority of clustering algorithms depend on the all-in assumption, in which all objects belong ...
Edgar González, Jordi Turmo
ML
2015
ACM
5 years 7 months ago
Random projections as regularizers: learning a linear discriminant from fewer observations than dimensions
We prove theoretical guarantees for an averaging-ensemble of randomly projected Fisher Linear Discriminant classifiers, focusing on the case when there are fewer training observat...
Robert J. Durrant, Ata Kabán
ML
2015
ACM
5 years 7 months ago
Soft-max boosting
The standard multi-class classification risk, based on the binary loss, is rarely directly minimized. This is due to (i) the lack of convexity and (ii) the lack of smoothness (and...
Matthieu Geist
ML
2015
ACM
5 years 7 months ago
Linearized alternating direction method with parallel splitting and adaptive penalty for separable convex programs in machine le
Many problems in statistics and machine learning (e.g., probabilistic graphical model, feature extraction, clustering and classification, etc) can be (re)formulated as linearly c...
Zhouchen Lin, Risheng Liu, Huan Li
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