Statistical Learning and Data Sciences

1. Front Matter

   Pages I-XIV

   PDF

2. Invited Papers

   1. Front Matter

      Pages 1-1

      PDF

   2. Learning with Intelligent Teacher: Similarity Control and Knowledge Transfer

      • Vladimir Vapnik, Rauf Izmailov

      Pages 3-32

   3. Statistical Inference Problems and Their Rigorous Solutions

      • Vladimir Vapnik, Rauf Izmailov

      Pages 33-71

3. Statistical Learning and Its Applications

   1. Front Matter

      Pages 73-73

      PDF

   2. Feature Mapping Through Maximization of the Atomic Interclass Distances

      • Savvas Karatsiolis, Christos N. Schizas

      Pages 75-85

   3. Adaptive Design of Experiments for Sobol Indices Estimation Based on Quadratic Metamodel

      • Evgeny Burnaev, Ivan Panin

      Pages 86-95

   4. GoldenEye++: A Closer Look into the Black Box

      • Andreas Henelius, Kai Puolamäki, Isak Karlsson, Jing Zhao, Lars Asker, Henrik Boström et al.

      Pages 96-105

   5. Gaussian Process Regression for Structured Data Sets

      • Mikhail Belyaev, Evgeny Burnaev, Yermek Kapushev

      Pages 106-115

   6. Adaptive Design of Experiments Based on Gaussian Processes

      • Evgeny Burnaev, Maxim Panov

      Pages 116-125

   7. Forests of Randomized Shapelet Trees

      • Isak Karlsson, Panagotis Papapetrou, Henrik Boström

      Pages 126-136

   8. Aggregation of Adaptive Forecasting Algorithms Under Asymmetric Loss Function

      • Alexey Romanenko

      Pages 137-146

   9. Visualization and Analysis of Multiple Time Series by Beanplot PCA

      • Carlo Drago, Carlo Natale Lauro, Germana Scepi

      Pages 147-155

   10. Recursive SVM Based on TEDA

       • Dmitry Kangin, Plamen Angelov

       Pages 156-168

   11. RDE with Forgetting: An Approximate Solution for Large Values of \(k\) with an Application to Fault Detection Problems

       • Clauber Gomes Bezerra, Bruno Sielly Jales Costa, Luiz Affonso Guedes, Plamen Parvanov Angelov

       Pages 169-178

   12. Sit-to-Stand Movement Recognition Using Kinect

       • Erik Acorn, Nikos Dipsis, Tamar Pincus, Kostas Stathis

       Pages 179-192

   13. Additive Regularization of Topic Models for Topic Selection and Sparse Factorization

       • Konstantin Vorontsov, Anna Potapenko, Alexander Plavin

       Pages 193-202

   14. Social Web-Based Anxiety Index’s Predictive Information on S&P 500 Revisited

       • Rapheal Olaniyan, Daniel Stamate, Doina Logofatu

       Pages 203-213

   15. Exploring the Link Between Gene Expression and Protein Binding by Integrating mRNA Microarray and ChIP-Seq Data

       • Mohsina Mahmuda Ferdous, Veronica Vinciotti, Xiaohui Liu, Paul Wilson

       Pages 214-222

   16. Evolving Smart URL Filter in a Zone-Based Policy Firewall for Detecting Algorithmically Generated Malicious Domains

       • Konstantinos Demertzis, Lazaros Iliadis

       Pages 223-233

This book constitutes the refereed proceedings of the Third International Symposium on Statistical Learning and Data Sciences, SLDS 2015, held in Egham, Surrey, UK, April 2015.
The 36 revised full papers presented together with 2 invited papers were carefully reviewed and selected from 59 submissions. The papers are organized in topical sections on statistical learning and its applications, conformal prediction and its applications, new frontiers in data analysis for nuclear fusion, and geometric data analysis.

• artificial neural networks
• big data
• classification
• computational complexity
• conformal predictors
• data analysis
• data mining
• data sciences
• evolving system
• genetic programming
• information security
• machine learning
• model selection
• optimization
• particle swarm optimization
• regularization
• statistical inference
• statistical learning
• support vector machines
• time series forecasting

• University of London, Egham, United Kingdom

  Alexander Gammerman, Vladimir Vovk

• Frederick University, Nicosia, Cyprus

  Harris Papadopoulos

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