Machine Learning and Data Mining in Pattern Recognition

1. Front Matter

   Pages I-IX

   PDF

2. An Information Retrieval Approach for Finding Dependent Subspaces of Multiple Views

   • Ziyuan Lin, Jaakko Peltonen

   Pages 1-16

3. Predicting Target Events in Industrial Domains

   • Julio Borges, Martin A. Neumann, Christian Bauer, Yong Ding, Till Riedel, Michael Beigl

   Pages 17-31

4. Importance of Recommendation Policy Space in Addressing Click Sparsity in Personalized Advertisement Display

   • Sougata Chaudhuri, Georgios Theocharous, Mohammad Ghavamzadeh

   Pages 32-46

5. Global Flow and Temporal-Shape Descriptors for Human Action Recognition from 3D Reconstruction Data

   • Georgios Th. Papadopoulos, Petros Daras

   Pages 47-62

6. Reverse Engineering Gene Regulatory Networks Using Sampling and Boosting Techniques

   • Turki Turki, Jason T. L. Wang

   Pages 63-77

7. Detecting Large Concept Extensions for Conceptual Analysis

   • Louis Chartrand, Jackie C. K. Cheung, Mohamed Bouguessa

   Pages 78-90

8. Qualitative and Descriptive Topic Extraction from Movie Reviews Using LDA

   • Christophe Dupuy, Francis Bach, Christophe Diot

   Pages 91-106

9. Towards an Efficient Method of Modeling “Next Best Action” for Digital Buyer’s Journey in B2B

   • Anit Bhandari, Kiran Rama, Nandini Seth, Nishant Niranjan, Parag Chitalia, Stig Berg

   Pages 107-116

10. Detecting Relative Anomaly

    • Richard Neuberg, Yixin Shi

    Pages 117-131

11. Optimization for Large-Scale Machine Learning with Distributed Features and Observations

    • Alexandros Nathan, Diego Klabjan

    Pages 132-146

12. CCPM: A Scalable and Noise-Resistant Closed Contiguous Sequential Patterns Mining Algorithm

    • Yacine Abboud, Anne Boyer, Armelle Brun

    Pages 147-162

13. Sparse Dynamic Time Warping

    • Youngha Hwang, Saul B. Gelfand

    Pages 163-175

14. Improving a Bayesian Decision Model for Supporting Diagnosis of Alzheimer’s Disease and Related Disorders

    • Carolina Medeiros Carvalho, Flávio Luiz Seixas, Aura Conci, Débora Christina Muchaluat-Saade, Jerson Laks

    Pages 176-191

15. Over-Fitting in Model Selection with Gaussian Process Regression

    • Rekar O. Mohammed, Gavin C. Cawley

    Pages 192-205

16. Machine Learning-as-a-Service and Its Application to Medical Informatics

    • Ahmad P. Tafti, Eric LaRose, Jonathan C. Badger, Ross Kleiman, Peggy Peissig

    Pages 206-219

17. Anomaly Detection from Kepler Satellite Time-Series Data

    • Nathaniel Grabaskas, Dong Si

    Pages 220-232

18. Prediction of Insurance Claim Severity Loss Using Regression Models

    • Ruth M. Ogunnaike, Dong Si

    Pages 233-247

19. A Spectral Clustering Method for Large-Scale Geostatistical Datasets

    • Francky Fouedjio

    Pages 248-261

20. Vulnerability of Deep Reinforcement Learning to Policy Induction Attacks

    • Vahid Behzadan, Arslan Munir

    Pages 262-275

This book constitutes the refereed proceedings of the 13th International Conference on Machine Learning and Data Mining in Pattern Recognition, MLDM 2017, held in New York, NY, USA in July/August 2017.The 31 full papers presented in this book were carefully reviewed and selected from 150 submissions. The topics range from theoretical topics for classification, clustering, association rule and pattern mining to specific data mining methods for the different multi-media data types such as image mining, text mining, video mining, and Web mining.

• data mining
• machine learning
• natural language processing
• social network analysis
• topic modeling
• anomaly detection
• association rule
• bayesian network
• big data
• case-based reasoning
• cellular networks
• clustering
• ensemble methods
• feature extraction
• fuzzy logic
• information retrieval
• sequential pattern mining
• similarity measures
• spatial data mining
• SVM

• Institute of Computer Vision and Applied Computer Sciences, Leipzig, Germany

  Petra Perner

Policies and ethics