Thursday, January 29, 2015
Tuesday, January 27, 2015
This paper summarizes definitions and uses cases for the sharing location information via geo messages. Geo messages let users of location based systems share location information as signatures to the standard messages (e.g., email, SMS).
Monday, January 26, 2015
Saturday, January 24, 2015
Friday, January 23, 2015
Monday, January 19, 2015
Friday, January 16, 2015
This paper presents a new approach for hyperlocal data sharing and delivery on the base of discoverable Bluetooth nodes. Our approach allows customers to associate user-defined data with network nodes and use a special mobile application (context-aware browser) for presenting this information to mobile users in proximity. Alternatively, mobile services can request and share local data in M2M applications rely on network proximity. Bluetooth nodes in cars are among the best candidates for the role of the bearing nodes.
P.S. You can see also our presentation
Thursday, January 15, 2015
Friday, January 09, 2015
1. The LION Way: Machine Learning plus Intelligent Optimization
Author/s: Roberto Battiti, Mauro Brunato
Publisher: Lionsolver, Inc., 2013
Learning and Intelligent Optimization (LION) is the combination of learning from data and optimization applied to solve complex problems. This book is about increasing the automation level and connecting data directly to decisions and actions.
2. A Course in Machine Learning
Author/s: Hal Daumé III
Publisher: ciml.info, 2012
This is a set of introductory materials that covers most major aspects of modern machine learning (supervised and unsupervised learning, large margin methods, probabilistic modeling, etc.). It's focus is on broad applications with a rigorous backbone.
3. A First Encounter with Machine Learning
Author/s: Max Welling
Publisher: University of California Irvine, 2011
The book you see before you is meant for those starting out in the field of machine learning, who need a simple, intuitive explanation of some of the most useful algorithms that our field has to offer. A prelude to the more advanced text books.
4. Bayesian Reasoning and Machine Learning
Author/s: David Barber
Publisher: Cambridge University Press, 2011
The book is designed for final-year undergraduate students with limited background in linear algebra and calculus. Comprehensive and coherent, it develops everything from basics to advanced techniques within the framework of graphical models.
5. Introduction to Machine Learning
Author/s: Amnon Shashua
Publisher: arXiv, 2009
Introduction to Machine learning covering Statistical Inference (Bayes, EM, ML/MaxEnt duality), algebraic and spectral methods (PCA, LDA, CCA, Clustering), and PAC learning (the Formal model, VC dimension, Double Sampling theorem).
6. The Elements of Statistical Learning: Data Mining, Inference, and Prediction
Author/s: T. Hastie, R. Tibshirani, J. Friedman - Springer, 2009
This book brings together many of the important new ideas in learning, and explains them in a statistical framework. The authors emphasize the methods and their conceptual underpinnings rather than their theoretical properties.
7. Reinforcement Learning
Author/s: C. Weber, M. Elshaw, N. M. Mayer
Publisher: InTech, 2008
This book describes and extends the scope of reinforcement learning. It also shows that there is already wide usage in numerous fields. Reinforcement learning can tackle control tasks that are too complex for traditional controllers.
8. Machine Learning
Author/s: Abdelhamid Mellouk, Abdennacer Chebira
Publisher: InTech, 2009
Neural machine learning approaches, Hamiltonian neural networks, similarity discriminant analysis, machine learning methods for spoken dialogue simulation and optimization, linear subspace learning for facial expression analysis, and more.
9. Reinforcement Learning: An Introduction
Author/s: Richard S. Sutton, Andrew G. Barto
Publisher: The MIT Press, 1998
The book provides a clear and simple account of the key ideas and algorithms of reinforcement learning. It covers the history and the most recent developments and applications. The only necessary mathematical background are concepts of probability.
10. Gaussian Processes for Machine Learning
Author/s: Carl E. Rasmussen, Christopher K. I. Williams
Publisher: The MIT Press, 2005
Gaussian processes provide a principled, practical, probabilistic approach to learning in kernel machines. The treatment is comprehensive and self-contained, targeted at researchers and students in machine learning and applied statistics.
11. Machine Learning, Neural and Statistical Classification
Author/s: D. Michie, D. J. Spiegelhalter
Publisher: Ellis Horwood, 1994
The book provides a review of different approaches to classification, compares their performance on challenging data-sets, and draws conclusions on their applicability to realistic industrial problems. A wide variety of approaches has been taken.
12. Introduction To Machine Learning
Author/s: Nils J Nilsson, 1997
This book concentrates on the important ideas in machine learning, to give the reader sufficient preparation to make the extensive literature on machine learning accessible. The author surveys the important topics in machine learning circa 1996.
13. Inductive Logic Programming: Techniques and Applications
Author/s: Nada Lavrac, Saso Dzeroski
Publisher: Prentice Hall, 1994
This book is an introduction to inductive logic programming. It covers empirical inductive logic programming with applications in knowledge acquisition, inductive program synthesis, inductive data engineering, and knowledge discovery in databases.
14. Practical Artificial Intelligence Programming in Java
Author/s: Mark Watson
Publisher: Lulu.com, 2008
The book uses the author's libraries and the best of open source software to introduce AI (Artificial Intelligence) technologies like neural networks, genetic algorithms, expert systems, machine learning, and NLP (natural language processing).
15. Information Theory, Inference, and Learning Algorithms
Author/s: David J. C. MacKay
Publisher: Cambridge University Press, 2003
A textbook on information theory, Bayesian inference and learning algorithms, useful for undergraduates and postgraduates students, and as a reference for researchers. Essential reading for students of electrical engineering and computer science.
The journal is published by the OIT Lab (Open Information Technologies Lab, Faculty of Computational Mathematics and Cybernetics, Lomonosov Moscow State University).
Indexing: Google Scholar, DOAJ, Ulrich's Periodicals Directory, ResearchBib, Elibrary.ru (RINC)
See also our archive