ECML/PKDD2011気になった論文リスト

自分用メモ．
当日，チェックしておきたいAccepted Papersを以下に纏めておきます．
ペーパーが公開されているものは，アブストをナナメ読みした感想を簡単に書いています．

Frequency-aware Truncated methods for Sparse Online Learning

  • Hidekazu Oiwa, Shin Matsushima, Hiroshi Nakagawa
  • 手前味噌ですが，自分達の論文．

Active learning with evolving streaming data

  • Indrė Žliobaitė, Albert Bifet, Bernhard Pfahringer, Geoff Holmes
  • ストリームデータ環境での能動学習．

Manifold Coarse Graining for Online Semi-Supervised Learning

  • Mehrdad Farajtabar, Amirreza Shaban, Hamid Rabiee, Mohammad Hossein Rohban
  • 初見では全くわからない．既存手法より少ないデータ数で，多様体の構造を保ったままで最適解を求めることが可能なアルゴリズムを提案しているらしい．

A boosting approach to multiview classification with cooperation

  • Sokol Koço, Cécile Capponi
  • Multiview Classificationに対するboosting手法．

On the Stratification of Multi-Label Data

  • Konstantinos Sechidis, Grigorios Tsoumakas, Ioannis Vlahavas
  • stratification samplingをマルチラベルなデータに適応する手法，及びその性能を評価．

Compact Coding for Hyperplane Classifiers in Heterogeneous Environment

  • Hao Shao, Bin Tong, Einoshin Suzuki

Fast approximate text document clustering using Compressive Sampling

  • Laurence A. F. Park

Is there a best quality metric for graph clusters?

  • Hélio Almeida, Dorgival Guedes, Wagner Meira Jr, Mohammed Zaki

Active Supervised Domain Adaptation

  • Avishek Saha, Piyush Rai, Hal Daumé III, Suresh Venkatasubramanian, Scott DuVall
  • 能動学習とドメイン適応の融合．

Multi-Label Ensemble Learning

  • Chuan Shi, Xiangnan Kong, Philip S. Yu, Bai Wang

Fast projections onto L1,q-norm balls for grouped feature selection

  • Suvrit Sra

Feature Selection for Transfer Learning

  • Selen Uguroglu, Jaime Carbonell

Influence and Passivity in Social Media

  • Daniel M. Romero, Wojciech Galuba, Sitaram Asur, Bernardo A. Huberman

Learning Recommendations in Social Media Systems By Weighting Multiple Relations

Toward a Fair Review-Management System

  • Theodoros Lappas, Evimaria Terzi

Adaptive Boosting for Transfer Learning using Dynamic Updates

  • Samir Al-Stouhi, Chandan K. Reddy

Datum-Wise Classification: A Sequential Approach to Sparsity

  • Gabriel Dulac-Arnold, Ludovic Denoyer, Philippe Preux, Patrick Gallinari
  • L0正則化を導入したempirical riskを最小化する問題を解く（実際に解くのは緩和したもの）．アルゴリズムは各データに関して，予測の際に用いる特徴集合をあらかじめ抽出する形となる．
  • 一押し．

Transfer Learning With Adaptive Regularizers

  • Ulrich Rückert, Marius Kloft

Analyzing Word Frequencies in Large Text Corpora using Inter-arrival Times and Bootstrapping

  • Jefrey Lijffijt, Panagiotis Papapetrou, Kai Puolamäki, Heikki Mannila

A Game Theoretic Framework for Data Privacy Preservation in Recommender Systems

  • Maria Halkidi, Iordanis Koutsopoulos

Linear Discriminant Dimensionality Reduction

  • Quanquan Gu, Zhenhui Li, Jiawei Han