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

生存環(huán)境

Influence of Graph Construction on Semi-supervised Learningthms on a variety of graph construction methods and parameter values. The obtained results show that the mutual .-nearest neighbors (mutKNN) graph may be the best choice for adjacency graph construction while the RBF kernel may be the best choice for weighted matrix generation. In addition, mutKNN t

漫不經(jīng)心

Tractable Semi-supervised Learning of Complex Structured Prediction Modelsf using approximations, the approach is effective and yields good improvements in generalization performance over the plain supervised method. In addition, we demonstrate that our inference engine can be applied to other semi-supervised learning frameworks, and extends them to solve problems with co

Lethargic

Embedding with Autoencoder Regularizationr reconstruction error. It is worth mentioning that instead of operating in a batch mode as most of the previous embedding algorithms conduct, the proposed framework actually generates an . embedding model and thus supports incremental embedding efficiently. To show the effectiveness of EAER, we ada

emulsify

Discovering Skylines of Subgroup Setsthms, and the accuracy of the levelwise method. Furthermore, we show that the skylines can be used for the objective evaluation of subgroup set heuristics. Finally, we show characteristics of the obtained skylines, which reveal that different quality-diversity trade-offs result in clearly different

mastoid-bone

Difference-Based Estimates for Generalization-Aware Subgroup Discoveryions. We show, how this technique can be applied for the most popular interestingness measures for binary as well as for numeric target concepts. The novel bounds are incorporated in an efficient algorithm, which outperforms previous methods by up to an order of magnitude.

BANAL

Conference proceedings 2013statistical learning; semi-supervised learning; unsupervised learning; subgroup discovery, outlier detection and anomaly detection; privacy and security; evaluation; applications; and medical applications.

malapropism

patella

COST

Baidya Nath Saha,Gautam Kunapuli,Nilanjan Ray,Joseph A. Maldjian,Sriraam Natarajan

CREEK

Indraneel Mukherjee,Kevin Canini,Rafael Frongillo,Yoram Singer