Titlebook: Discovery Science; 22nd International C Petra Kralj Novak,Tomislav Šmuc,Sašo Džeroski Conference proceedings 2019 Springer Nature Switzerla

Fillmore

书目名称Discovery Science影响因子(影响力)




书目名称Discovery Science影响因子(影响力)学科排名




书目名称Discovery Science网络公开度




书目名称Discovery Science网络公开度学科排名




书目名称Discovery Science被引频次




书目名称Discovery Science被引频次学科排名




书目名称Discovery Science年度引用




书目名称Discovery Science年度引用学科排名




书目名称Discovery Science读者反馈




书目名称Discovery Science读者反馈学科排名

悦耳

吸引力

Utilizing Hierarchies in Tree-Based Online Structured Output Predictionhy. We design the experimental setup to ascertain whether the additional information contained in the hierarchy can be utilized to improve the predictive performance in the leaf targets. The proposed method shows promising results, producing potential improvements that should be investigated further.

SLING

PSA-velocity

主讲人

https://doi.org/10.1057/9780230510418of biclustering algorithms is proposed using FCA and pattern structures, an extension of FCA for dealing with numbers and other complex data. Several types of biclusters – constant-column, constant-row, additive, and multiplicative – and their relation to interval pattern structures is presented.

主讲人

Conference proceedings 2019he 21 full and 19 short papers presented together with 3 abstracts of invited talks in this volume were carefully reviewed and selected from 63 submissions. The scope of the conference includes the development and analysis of methods for discovering scientific knowledge, coming from machine learning

Nibble

A Unified Approach to Biclustering Based on Formal Concept Analysis and Interval Pattern Structureof biclustering algorithms is proposed using FCA and pattern structures, an extension of FCA for dealing with numbers and other complex data. Several types of biclusters – constant-column, constant-row, additive, and multiplicative – and their relation to interval pattern structures is presented.

忘川河

令人作呕

A Sampling-Based Approach for Discovering Subspace Clusters is then mined for frequent itemsets, which we show can be translated back to subspace clusters. In our extensive experimental analysis, we show on synthetic as well as real world data that our method is capable of discovering highly interesting subspace clusters.