Titlebook: Discovery Science; 22nd International C Petra Kralj Novak,Tomislav ?muc,Sa?o D?eroski Conference proceedings 2019 Springer Nature Switzerla

Fillmore

書目名稱Discovery Science影響因子(影響力)




書目名稱Discovery Science影響因子(影響力)學(xué)科排名




書目名稱Discovery Science網(wǎng)絡(luò)公開度




書目名稱Discovery Science網(wǎng)絡(luò)公開度學(xué)科排名




書目名稱Discovery Science被引頻次




書目名稱Discovery Science被引頻次學(xué)科排名




書目名稱Discovery Science年度引用




書目名稱Discovery Science年度引用學(xué)科排名




書目名稱Discovery Science讀者反饋




書目名稱Discovery Science讀者反饋學(xué)科排名

悅耳

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

主講人

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.

主講人

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

令人作嘔

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.