ENGLISH    |  



データマイニング (DM) と機械学習 (ML) とリンク解析 (LA) に関する勉強会 (旧称 DMML; 2004/10 より DMLA に変更).

火曜日: データマイニング/機械学習/リンク解析関連の本を輪講
金曜日: データマイニング/機械学習/リンク解析関連の文献紹介


時間: 火曜日, 金曜日 19:00-
場所: A707



03/04 (金) 持橋

David Cohn, Thomas Hofmann.

The Missing Link: a probabilistic model of document content and hypertext connectivity.

In Advances in Neural Information Processing Systems 13 (NIPS 2001), MIT Press, 2002.


David Cohn, Huan Chang.

Learning to Probabilistically Identify Authoritative Documents.

In Proc. 17th Intl. Conference on Machine Learning (ICML-2000), 2000.


02/18 (金) 原

Koji Tsuda, Motoaki Kawanabe, Klaus-Robert Meller

Clustering with the Fisher score

In Advances in Neural Information Processing Systems 15 (NIPS 2002), MIT Press, 2003.

02/04 (金) 竹原

Michal Rosen-Zvi, Thomas Griffiths, Mark Steyvers, Padhraic Smyth.

The Author-Topic Model for Authors and Documents.

In Proc. 20th Conference on Uncertainty in Artificial Intelligence (UAI 2004), 2004.


01/14 (金) 福岡

S. Amari and S. Wu.

Improving support vector machine classifiers by modifying kernel functions.

Neural Networks Vol.12, No.6, pp.783-789, 1999.

01/07 (金) 伊藤

Olivier Chapelle, Jason Weston and Bernhard Schoelkopf.

Cluster kernels for semi-supervised learning.

In Advances in Neural Information Processing Systems 15 (NIPS 2002), MIT Press, 2003.

12/17 (金) 東

Thomas G. Dietterich, Adam Ashenfelter and Yaroslav Bulatov.

Training Conditional Random Fields via gradient tree boosting.

In Proc. 21st Intl. Conference on Machine Learning (ICML-2004), 2004.

12/10 (金) 新保

Stella X. Yu and Jianbo Shi.

Multiclass spectral clustering.

In Proc. IEEE International Conference on Computer Vision, 2003.

11/26 (金) 鈴木

C. Cortes, P. Haffner, and M. Mohri.

Rational Kernels.

In Advances in Neural Information Processing Systems 15 (NIPS 2002), MIT Press, 2003.

11/24 (水) 伊藤

Abba Krieger, Chuan Long, and Abraham Wyner.

Boosting noisy data.

In Proc. 18th International Conference on Machine Learning (ICML-2001), 2001.

11/19 (金) 松本(裕)

I.S.Dhillon, Y.Guan, and B.Kulis.

Kernel k-means, spectral clustering and normalized cuts.

In Proc. Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2004.


11/17 (水) 新保

Mikhail Bilenko and Sugato Basu and Raymond J. Mooney.

Integrating constraints and metric learning in semi-supervised clustering.

In Proc. 21st International Conference on Machine Learning (ICML-2004), pp. 81-88, 2004.


11/12 (金) 工藤 (NTT)

Y. Weiss.

Comparing the mean field method and belief propagation for approximate inference in MRFs.

In Advanced Mean Field Methods--Theory and Practice, MIT Press, Cambridge, MA, 2001.


11/05 (金) 福岡(健)

Rehan Akbani, Stephen Kwek, and Nathalie Japkowicz

Applying Support Vector Machines to imbalanced datasets.

In Proc. European Conference on Machine Learning (ECML 2004), 2004.


10/22 (金) 伊藤

A. Blum and S. Chawla.

Learning from labeled and unlabled data using graph mincut.

In Proc. Intl. Conference on Machine Learning (ICML-2001), 2001.

10/15 (金) 東

ME, CRF 等で用いられる各種準 Newton 法について.

Robert Malouf.

A comparison of algorithms for maximum entropy parameter estimation.

In Proc. CoNLL 2002.

Jorge Nocedal and Stephen J. Wright.

Numerical Optimization. Springer, 1999.

Chapters 8 (Quasi-Newton Methods) and 9 (Large-Scale Quasi-Newton and Partially Separable Optimization).

10/08 (金) 新保

Jun Sese and Shinichi Morishita.

Itemset classified clustering.

In Proc. 8th European Conference on Principles and Practice of Knowledge Discovery in Databases (PKDD 2004), LNAI 3202, Springer, pp. 398-409, 2004.

07/08 (木) 松本(裕)

Charles Sutton, Khashayar Rohanimanesh, and Andrew MaCallum.

Dynamic conditional random field: factorized probabilistic models for labeling and segmenting sequence data.

To appear in Proc. 21st Intl. Conference on Machine Learning (ICML-2004).


07/02 (金) 工藤 (NTT)

J. Wang and J. Han,

BIDE: efficient mining of frequent closed sequences.

In Proc. 2004 Intl. Conf. on Data Engineering (ICDE'04).


PrefixSpan の拡張で closed pattern (極大パターン) をマイニング. 冗長なパターンがたくさんとれるのを回避.

06/18 (金) 新保

Vassilis Athitsos, Jonathan Alon, Stan Sclaroff, and George Kollios.

BoostMap: a method for efficient approximate similarity rankings.

To appear in Proc. IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), Washington, DC, USA, June 2004.

06/04 (金) 藤田(篤)

Xiaoli Zhang Fern and Carla E. Brodley.

Random projection for high dimensional data clustering: a cluster ensemble approach.

In Proc. 20th Intl. Conference on Machine Learning (ICML-2003), Washington, DC, USA, 2003, pp.186-193.

05/28 (金) 伊藤

Judy Johnson, Kostas Tsioutsiouliklis, and C. Lee Giles.

Evolving strategies for focused Web crawling.

In Proc. 20th Intl. Conference on Machine Learning (ICML-2003), Washington, DC, USA, 2003, pp.298-305.

05/21 (金) 工藤 (NTT)

X. Yan and J. Han.

gSpan: graph-based substructure pattern mining.

Technical Report, UIUCDCS-R-2002-2296, University of Illinois, Urbana-Champaign.


A shorter version appeared in Proc. IEEE 2002 Intl. Conf. on Data Mining (ICDM'02), Maebashi, Japan.

05/14 (金) 東

Ruslan Salakhutdinov, Sam Roweis, and Zoubin Gharamani.

Optimization with EM and expectation-conjugate-gradient.

In Proc. 20th International Conference on Machine Learning (ICML-2003), Washington, DC, USA, 2003.

05/07 (金) 新保

James T. Kwok and Ivor W. Tsang.

The pre-image problem in kernel methods.

In Proc. 20th International Conference on Machine Learning (ICML-2003), Washington, DC, USA, 2003, pp.408-415.

04/13 (火) 伊藤

Alan Borodin, Gareth O. Roberts, Jeffrey S. Rosenthal, and Panayiotis Tsaparas.

Finding authorities and hubs from link structures on the World Wide Web.

In Proc. International World Wide Web Conference, pp.415-429, 2001.

輪講: The Elements of Statistical Learning

  • 分担

Chapter 1 Introduction

Chapter 2 Overview of Supervised Learning - Eric

Chapter 3 Linear Methods for Regression

Chapter 4 Linear Methods for Classification

Chapter 5 Basis Expansions and Regularization

Chapter 6 Kernel Methods

Chapter 7 Model Assessment and Selection

Chapter 8 Model Inference and Averaging - 伊藤

Chapter 9 Additive Models, Trees, and Related Methods

Chapter 10 Boosting and Additive Trees

Chapter 11 Neural Networks

Chapter 12 Support Vector Machines and Flexible Discriminants

Chapter 13 Prototype Methods and Nearest-Neighbors - 浅原

Chapter 14 Unsupervised Learning

11/09 (火) Eric

  • Chapter 2. Section 2.5-2.6.

10/26 (火) 浅原, Eric

  • Chapter 13, pp.427-.
  • Chapter 2. Section 2.4.

10/19 (火) Eric

  • Chapter 2 Overview of Supervised Learning. Section 2.3 まで.

10/12 (火) 浅原

  • Chapter 13 Prototype Methods and Nearest-Neighbors, Section 13.3 まで (pp.411-427).

輪講: Machine Learning: Discriminative and Generative

  • Tony Jebara. Machine Learning: Discriminative and Generative. Kluwer Academic Publishers, 2004.
  • 分担

Chapter 1 松本(裕)

Chapter 2 高橋, 玉森, 松本(吉)

Chapter 3 伊藤, 飯田 小林(の)

Chapter 4 原, 東, 新保

Chapter 5 藤田(篤), 松本(裕), 浅原

07/20 (火) 伊藤

  • Chapter 3, Sections 9.2-10.3 (pp.84-98)

07/13 (火) 飯田

  • Chapter 3, Sections 7-9.1 (pp.78-84)

07/06 (火) 小林(の), 飯田

  • Chapter 3, Sections 4-6 (pp.72-77)

06/29 (火) 小林(の)

  • Chapter 3, Sections 1.2-3 (pp.65-71)

06/22 (火) 高橋, 小林(の)

  • Chapter 2, Sections 4.5-6 (pp.55-60)
  • Chapter 3 Maximum Entropy Discrimination, Section 1.1 まで (pp.61-65).

06/15 (火) 高橋

  • Chapter 2, Sections 4-4.4 (pp.48-55)

06/08 (火) 松本(吉), 高橋

  • Chapter 2, Sections 2.6-3.3 (pp.41-48)

06/01 (火) 新保

05/25 (火) 松本(吉)

  • Chapter 2, Sections 2.5-2.6 (pp.35-40)

05/18 (火) 玉森, 松本(吉)

  • Chapter 2, Sections 2.3 - 2.5 (pp.17-35)

05/11 (火) 玉森

  • Chapter 2 Generative versus discriminative learning, Section 2.2 (p.25) まで.

04/27 (火) 松本(裕)

  • Chapter 1 Introduction, pp.1-16