BN.com Gift Guide

Advances in Knowledge Discovery and Data Mining, Part I: 16th Pacific-Asia Conference, PAKDD 2012, Kuala Lumpur, Malaysia, May 29-June1, 2012, Proceedings, Part I

Overview

The LNAI series reports state-of-the-art results in artificial intelligence research, development, and education, at a high level and in both printed and electronic form, Enjoying light cooperation with the R&D community, with numerous individuals, as well as with prestigious organizations and societies, LNA1 has grown into the most comprehensive artificial intelligence research forum available.

The scope of LNAI spans the whole range of artificial intelligence and ...

See more details below
Paperback (2012)
$103.20
BN.com price
(Save 20%)$129.00 List Price
Other sellers (Paperback)
  • All (11) from $89.90   
  • New (8) from $92.99   
  • Used (3) from $89.90   
Sending request ...

Overview

The LNAI series reports state-of-the-art results in artificial intelligence research, development, and education, at a high level and in both printed and electronic form, Enjoying light cooperation with the R&D community, with numerous individuals, as well as with prestigious organizations and societies, LNA1 has grown into the most comprehensive artificial intelligence research forum available.

The scope of LNAI spans the whole range of artificial intelligence and intelligent information processing including interdisciplinary topics in a variety of application fields. The type of material published traditionally includes

proceedings (published in time for (he respective conference)

post-proceedings (consisting of thoroughly revised final full papers)

research monographs (which may be based on PhD work)

More recently, several color-cover sublines have been added featuring, beyond a collection of papers, various added-value components; these sublines include

tutorials (textbook-like monographs or collections of lectures given at advanced courses)

state-of-the-art surveys (offering complete and mediated coverage of a topic)

hot topics (introducing emergent topics to the broader community)

In parallel to the printed book, each new volume is published electronically in LNCS Online.

Detailed information on LNCS can be found at springer.com/lncs

Proposals for publication should be sent to LNCS Editorial, Tiergartenstr. 17, 69121 Heidelberg, Germany E-mail: lncs@springer.com

Read More Show Less

Product Details

Table of Contents

Supervised Learning: Active, Ensemble, Rare-Class and Online

Time-Evolving Relational Classification and Ensemble Methods Ryan Rossi Jennifer Neville 1

Active Learning for Hierarchical Text Classification Xiao Li Da Kuang Charles X. Ling 14

TeamSkill Evolved: Mixed Classification Schemes for Team-Based Multi-player Games Colin DeLong Jaideep Srivastava 26

A Novel Weighted Ensemble Technique for Time Series Forecasting Ratnadip Adhikari R.K. Agrawal 38

Techniques for Efficient Learning without Search Houssam Salem Pramuditha Suraweera Geoffrey I. Webb Janice R. Boughton 50

An Aggressive Margin-Based Algorithm for Incremental Learning JuiHsi Fu Sing Ling Lee 62

Two-View Online Learning Tarn T. Nguyen Kuiyu Chang Siu Cheung Hui 74

A Generic Classifier-Ensemble Approach for Biomedical Named Entity Recognition Zhihua Liao Zili Zhang 86

Neighborhood Random Classification Djamel Abdelkader Zighed Diala Ezzeddine Fabien Rico 98

SRF: A Framework for the Study of Classifier Behavior under Training Set Mislabeling Noise Katsiaryna Mirylenka George Giannakopoulos Themis Palpanas 109

Building Decision Trees for the Multi-class Imbalance Problem T. Ryan Hoens Qi Qian Nitesh V. Chawla Zhi-Hua Zhou 122

Scalable Random Forests for Massive Data Bingguo Li Xiaojun Chen Mark Junjie Li Joshua Zhexue Huang Shengzhong Feng 135

Hybrid Random Forests: Advantages of Mixed Trees in Classifying Text Data Baoxun Xu Joshua Zhexue Huang Graham Williams Mark Junjie Li Yunming Ye 147

Learning Tree Structure of Label Dependency for Multi-label Learning Bin Fu Zhihai Wang Rong Pan Guandong Xu Peter Dolog 159

Multiple Instance Learning for Group Record Linkage Zhichun Fu Jun Zhou Peter Christen Mac Boot 171

Incremental Set Recommendation Based on Class Differences Yasuyuki Shirai Koji Tsuruma Yuko Sakurai Satoshi Oyama Shin-ichi Minato 183

Active Learning for Cross Language Text Categorization Yue Liu Lin Dai Weitao Zhou Heyan Huang 195

Evasion Attack of Multi-class Linear Classifiers Han Xiao Thomas Stibor Claudia Eckert 207

Foundation of Mining Class-Imbalanced Data Da Kuang Charles X. Ling Jun Du 219

Active Learning with c-Certainty Eileen A. Ni Charles X. Ling 231

A Term Association Translation Model for Naive Bayes Text Classification Meng-Sung Wu Hsin-Min Wang 243

A Double-Ensemble Approach for Classifying Skewed Data Streams Chongsheng Zhang Paolo Soda 254

Generating Balanced Classifier-Independent Training Samples from Unlabeled Data Youngja Park Zijie Qi Suresh N. Chari Ian M. Molloy 266

Nyström Approximate Model Selection for LSSVM Lizhong Ding Shizhong Liao 282

Exploiting Label Dependency for Hierarchical Multi-label Classification Noor Alaydie Ghandan K. Reddy Farshad Fotouhi 294

Diversity Analysis on Boosting Nominal Concepts Nida Meddouri Héla Khoufi Mondher Sadok Maddouri 306

Extreme Value Prediction for Zero-Inflated Data Fan Xin Zubin Abraham 318

Learning to Diversify Expert Finding with Subtopics Hang Su Jie Tang Wanling Hong 330

An Associative Classifier for Uncertain Datasets Metanat Hooshsadat Osmar R. Zaïane 342

Unsupervised Learning: Clustering, Probabilistic Modeling

Neighborhood-Based Smoothing of External Cluster Validity Measures Ken-ichi Fukui Masayuki Numao 354

Sequential Entity Group Topic Model for Getting Topic Flows of Entity Groups within One Document Young-Seob Jeong Ho-Jin Choi 366

Topological Comparisons of Proximity Measures Djamel Abdelkader Zighed Rafik Abdesselam Asmelash Hadgu 379

Quad-tuple PLSA: Incorporating Entity and Its Rating in Aspect Identification Wenjuan Luo Fuzhen Zhuang Qing He Zhongzhi Shi 392

Clustering-Based κ-Anonymity Xianmang He HuaHui Chen Yefang Chen Yihong Dong Peng Wang Zhenhua Huang 405

Unsupervised Ensemble Learning for Mining Top-n Outliers Jun Gao Weiming Hu Zhongfei(Mark) Zhang Ou Wu 418

Towards Personalized Context-Aware Recommendation by Mining Context Logs through Topic Models Kuifei Yu Baoxian Zhang Hengshu Zhu Huanhuan Cao Jilei Tian 431

Mining of Temporal Coherent Subspace Clusters in Multivariate Time Series Databases Hardy Kremer Stephan Günnemann Arne Held Thomas Seidl 444

A Vertex Similarity Probability Model for Finding Network Community Structure Kan Li Yin Pang 456

Hybrid-ε-greedy for Mobile Context-Aware Recommender System Djallel Bouneffouf Amel Bouzeghoub Alda Lopes Gançarski 468

Unsupervised Multi-label Text Classification Using a World Knowledge Ontology Xiaohui Tao Yuefeng Li Raymond Y.K. Lau Hua Wang 480

Semantic Social Network Analysis with Text Corpora Dong-mei Yang Hui Zheng Ji-kun Yan Ye Jin 493

Visualizing Clusters in Parallel Coordinates for Visual Knowledge Discovery Yang Xiang David Fuhry Ruoming Jin Ye Zhao Kun Huang 505

Feature Enriched Nonparametric Bayesian Co-clustering Pu Wang Carlotta Domeniconi Huzefa Rangwala Kathryn B. Laskey 517

Shape-Based Clustering for Time Series Data Warissara Meesrikamolkul Vit Niennattrakul Chotirat Ann Ratanamahatana 530

Privacy-Preserving EM Algorithm for Clustering on Social Network Bin Yang Issei Sato Hiroshi Nakagawa 542

Named Entity Recognition and Identification for Finding the Owner of a Home Page Vassilis Plachouras Matthieu Rivière Michalis Vazirgiannis 554

Clustering and Understanding Documents via Discrimination Information Maximization Malik Tahir Hassan Asim Karim 566

A Semi-supervised Incremental Clustering Algorithm for Streaming Data Maria Halkidi Myra Spiliopoulou Aikaterini Pavlou 578

Unsupervised Sparse Matrix Co-clustering for Marketing and Sales Intelligence Anastasios Zouzias Michail Vlachos Nikolaos M. Freris 591

Expectation-Maximization Collaborative Filtering with Explicit and Implicit Feedback Bin Wang Mohammadreza Rahimi Dequan Zhou Xin Wang 604

Author Index 617

Read More Show Less

Customer Reviews

Be the first to write a review
( 0 )
Rating Distribution

5 Star

(0)

4 Star

(0)

3 Star

(0)

2 Star

(0)

1 Star

(0)

Your Rating:

Your Name: Create a Pen Name or

Barnes & Noble.com Review Rules

Our reader reviews allow you to share your comments on titles you liked, or didn't, with others. By submitting an online review, you are representing to Barnes & Noble.com that all information contained in your review is original and accurate in all respects, and that the submission of such content by you and the posting of such content by Barnes & Noble.com does not and will not violate the rights of any third party. Please follow the rules below to help ensure that your review can be posted.

Reviews by Our Customers Under the Age of 13

We highly value and respect everyone's opinion concerning the titles we offer. However, we cannot allow persons under the age of 13 to have accounts at BN.com or to post customer reviews. Please see our Terms of Use for more details.

What to exclude from your review:

Please do not write about reviews, commentary, or information posted on the product page. If you see any errors in the information on the product page, please send us an email.

Reviews should not contain any of the following:

  • - HTML tags, profanity, obscenities, vulgarities, or comments that defame anyone
  • - Time-sensitive information such as tour dates, signings, lectures, etc.
  • - Single-word reviews. Other people will read your review to discover why you liked or didn't like the title. Be descriptive.
  • - Comments focusing on the author or that may ruin the ending for others
  • - Phone numbers, addresses, URLs
  • - Pricing and availability information or alternative ordering information
  • - Advertisements or commercial solicitation

Reminder:

  • - By submitting a review, you grant to Barnes & Noble.com and its sublicensees the royalty-free, perpetual, irrevocable right and license to use the review in accordance with the Barnes & Noble.com Terms of Use.
  • - Barnes & Noble.com reserves the right not to post any review -- particularly those that do not follow the terms and conditions of these Rules. Barnes & Noble.com also reserves the right to remove any review at any time without notice.
  • - See Terms of Use for other conditions and disclaimers.
Search for Products You'd Like to Recommend

Recommend other products that relate to your review. Just search for them below and share!

Create a Pen Name

Your Pen Name is your unique identity on BN.com. It will appear on the reviews you write and other website activities. Your Pen Name cannot be edited, changed or deleted once submitted.

 
Your Pen Name can be any combination of alphanumeric characters (plus - and _), and must be at least two characters long.

Continue Anonymously

    If you find inappropriate content, please report it to Barnes & Noble
    Why is this product inappropriate?
    Comments (optional)