Thursday, January 29, 2015

ConnecteDriver 2015


We continue to share links for monitoring the interesting events in Twitter. Now it is ConnecteDriver, Brussels 2015

Twitter Live Feed: ConnecteDriver 2015

/via Bluetooth Data Points

Tuesday, January 27, 2015

On Geo Signatures

Namiot, D. (2015). Geo Signature and its applications. International Journal of Information Science and Intelligent System, 4(1), 105-118.

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

Functional programming

A practical introduction into Funtional Programmimg

Friday, January 23, 2015

Learn Data Science

A really nice project: Learn data science in your browser. It is free and even no registration required.

Monday, January 19, 2015

DLD 2015


We continue to share links for monitoring the interesting events in Twitter. Now it is DLD Conference, Munich 2015

Twitter Live Feed: Munich 2015

/via Bluetooth Data Points

Friday, January 16, 2015

Cars as Tags

Our new paper:

Namiot, D., & Sneps-Sneppe, M. (2014, October). CAT??? cars as tags. In Communication Technologies for Vehicles (Nets4Cars-Fall), 2014 7th International Workshop on (pp. 50-53). IEEE.

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

Friday, January 09, 2015

Machine Learning ebooks

Free ML ebooks:

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.


INJOIT - call for papers

The International Journal of Open Information Technologies (INJOIT) is an all-electronic journal with the aim to bring the most recent and unpublished research and development results in the area of information technologies to the scientific and technical societies. Free, peer reviewed papers. English or Russian languages. It is free to publish your paper.

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

Monday, January 05, 2015

More about Time Series Databases

An actual question in connection with M2M and Smart Cities tasks - Open source databases for time-series, events and metrics. E.g.:

What is else?

Sunday, January 04, 2015

Cold emails

A great story about cold emails. And about a great product too.

Wednesday, December 31, 2014

Big Data for 2015

Rise of Data Virtualization

Hybrid Data Stores Become More Common

Semantics Becomes Standard

Hadoop Without Map-Reduce and Map-Reduce Without Hadoop

Databases Become Working Memory

Towards a Universal Data Query Language

Data Analytics Moves Beyond SQL

The JavaScript Stack Solidifies

from here.

I am not sure about sematics and I am not sure about Universal Data Query Language. Vice versa, I am sure they are not :)

Monday, December 29, 2014