Titlebook: Machine Learning and Knowledge Discovery in Databases; European Conference, Hendrik Blockeel,Kristian Kersting,Filip Železný Conference pro

启发

吹牛大王

失败主义者

0302-9743 dings of the European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2013, held in Prague, Czech Republic, in September 2013. The 111 revised research papers presented together with 5 invited talks were carefully reviewed and selected from 447 submissions. The papers

otic-capsule

Amnesty

Bundle CDN: A Highly Parallelized Approach for Large-Scale ℓ1-Regularized Logistic Regressionional Armijo line search to obtain the stepsize. By theoretical analysis on global convergence, we show that BCDN is guaranteed to converge with a high DOP. Experimental evaluations over five public datasets show that BCDN can better exploit parallelism and outperforms state-of-the-art algorithms in speed, without losing testing accuracy.

独行者

MORD: Multi-class Classifier for Ordinal Regressions on standard benchmarks as well as in solving a real-life problem. In particular, we show that the proposed piece-wise ordinal classifier applied to visual age estimation outperforms other standard prediction models.

斗争

钢盔

AR-Boost: Reducing Overfitting by a Robust Data-Driven Regularization Strategynables a natural extension to multiclass boosting, and further reduces overfitting in both the binary and multiclass cases. We derive bounds for training and generalization errors, and relate them to AdaBoost. Finally, we show empirical results on benchmark data that establish the robustness of our approach and improved performance overall.

Ceramic

Exploratory Learningres different numbers of classes while learning. “Exploratory” SSL greatly improves performance on three datasets in terms of F1 on the classes . seed examples—i.e., the classes which are expected to be in the data. Our Exploratory EM algorithm also outperforms a SSL method based non-parametric Bayesian clustering.

Bone-Scan

PSSDL: Probabilistic Semi-supervised Dictionary Learningictionary learning which uses both the labeled and unlabeled data. Experimental results demonstrate that the performance of the proposed method is significantly better than the state of the art dictionary based classification methods.