Titlebook: Machine Learning and Knowledge Discovery in Databases; European Conference, Albert Bifet,Michael May,Myra Spiliopoulou Conference proceedin

貧困

Listener-Aware Music Recommendation from Sensor and Social Media Dataindings on the topics of tailoring music recommendations to individual listeners and to groups of listeners sharing certain characteristics. We focus on two tasks: . (also known as serial recommendation) using sensor data and . using social media data.

Anal-Canal

Logic-Based Incremental Process Miningful framework for supporting all of the above. This paper presents a First-Order Logic incremental method for inferring process models. Its efficiency and effectiveness were proved with both controlled experiments and a real-world dataset.

miniature

978-3-319-23460-1Springer International Publishing Switzerland 2015

beta-cells

Machine Learning and Knowledge Discovery in Databases978-3-319-23461-8Series ISSN 0302-9743 Series E-ISSN 1611-3349

remission

avarice

Bayesian Hypothesis Testing in Machine LearningMost hypothesis testing in machine learning is done using the frequentist null-hypothesis significance test, which has severe drawbacks. We review recent Bayesian tests which overcome the drawbacks of the frequentist ones.

Bernstein-test

招致

Conference proceedings 2015ence mining; preference learning and label ranking; probabilistic, statistical, and graphical approaches; rich data; and social and graphs. Part III is structured in industrial track, nectar track, and demo track.

MITE

0302-9743 n and sequence mining; preference learning and label ranking; probabilistic, statistical, and graphical approaches; rich data; and social and graphs. Part III is structured in industrial track, nectar track, and demo track.978-3-319-23460-1978-3-319-23461-8Series ISSN 0302-9743 Series E-ISSN 1611-3349

羽飾