Titlebook: Discovery Science; 15th International C Jean-Gabriel Ganascia,Philippe Lenca,Jean-Marc Pet Conference proceedings 2012 Springer-Verlag Berl

cushion

Declarative Modeling for Machine Learning and Data Miningdata mining, in which the user specifies the problem in a high level modeling language and the system automatically transforms such models into a format that can be used by a solver to efficiently generate a solution. This should be much easier for the user than having to implement or adapt an algor

Gyrate

墻壁

反叛者

Fast Progressive Training of Mixture Models for Model Selection fast approximation of the Kullback-Leibler (KL) divergence as a criterion to merge the mixture components. The proposed methodology is used in mixture modelling of two chromosomal aberration datasets showing that model selection is efficient and effective.

單挑

Predicting Ramp Events with a Stream-Based HMM Frameworkto occur..We compare SHRED framework against Persistence baseline in predicting ramp events occurring in short-time horizons, ranging from 30 minutes to 90 minutes. SHRED consistently exhibits more accurate and cost-effective results than the baseline.

一大塊

Large Scale Spectral Clustering Using Resistance Distance and Spielman-Teng Solvers Spielman and Teng near-linear time solver for systems of linear equations and random projection. Experiments on several synthetic and real datasets show that the proposed approach has better clustering quality and is faster than the state-of-the-art approximate spectral clustering methods.

Gullible

不持續(xù)就爆

BLAND

Conquest

Including Spatial Relations and Scales within Sequential Pattern Extractional scales. We propose an algorithm, STR_PrefixGrowth, which can be applied to a huge amount of data. The proposed method is evaluated on hydrological data collected on the Sa?ne watershed during the last 19 years. Our experiments emphasize the contribution of our approach toward the existing methods.