Titlebook: Machine Learning and Knowledge Discovery in Databases; European Conference, Peter A. Flach,Tijl Bie,Nello Cristianini Conference proceeding

變化無常

entreat

Apraxia

Smoothing Categorical Dataperiments we show that our approach preserves the large scale structure of a dataset well. That is, the smoothed dataset is simpler while the original and smoothed datasets share the same large scale structure.

Left-Atrium

implore

Combining Subjective Probabilities and Data in Training Markov Logic Networkssly used Gaussian priors over weights. We show how one can learn weights in an MLN by combining subjective probabilities and training data, without requiring that the domain expert provides consistent knowledge. Additionally, we also provide a formalism for capturing conditional subjective probabili

WITH

Score-Based Bayesian Skill Learningns demonstrate that the new score-based models (a) provide more accurate win/loss probability estimates than TrueSkill when training data is limited, (b) provide competitive and often better win/loss classification performance than TrueSkill, and (c) provide reasonable score outcome predictions with

debris

Hypergraph Spectra for Semi-supervised Feature Selectionestablish a novel hypergraph framework which is used for characterizing the multiple relationships within a set of samples. Thus, the structural information latent in the data can be more effectively modeled. Secondly, we derive a hypergraph subspace learning view of feature selection which casting

HEED

欺騙手段

Machine Learning and Knowledge Discovery in DatabasesEuropean Conference,

CURT