Andreu Sancho Homepage

The increasing bulk of data generation in industrial and scientific applications has fostered practitioners’ interest in mining large amounts of unlabelled data in the form of continuous, high speed, and time-changing streams of information. An appealing field is association stream mining, which models dynamically complex domains via rules without assuming any a priori structure. Different from the related frequent pattern mining field, its goal is to extract interesting associations among the forming features of such data, adapting these to the ever-changing dynamics of the environment in a pure online fashion--without the typical offline rule generation. These rules are adequate for extracting valuable insight which helps in decision making. It is a pleasure to detail Fuzzy-CSar, an online genetic fuzzy system (GFS) designed to extract interesting, quantitative rules from streams of samples. It evolves its internal model online, being able to quickly adapt its knowledge in the

I am glad of sharing some very motivating news: after a little bit more than year, Kaggle hosts a new CERN/LHC competition ! This time it comes from the tau disintegration into three muons. Again, the challenge consists of identifying the pattern from both real and simulated data to identify this rare phenomenon. The training data set consists of 49 distinct real-valued features plus the signal (i.e., background noise and the proper event). This time I am using Python with Scikit learn, Pandas and XGBoost (this latter one by far my favorite ensemble package; also available for R!) and I have to confess that is much easier when you do not have to program everything from scratch (as I usually do in C/C++ or Java), and that I have more time to think in new ways of solving the problem from a pure data scientist view. However, I really adore making use of use my own programs (I ended up 24th out of 1691 in the Forest Cover Type challenge using my own Extra Trees/Random Forest progra

Neural nets, with their flexible data representation capable of approximate any arbitrary function, are in a new renaissance with the development of Deep Leaners. These stack many layers (typically dozens of quasi-independent layers) of traditional neurons to achieve something very remarkable: self detection of important features from input data. The application of Deep Learning is not restricted to raw classification or regression; these family of techniques are applied to much broader fields such as machine vision and speech recognition. Recent advances went much further and combined a Deep Learning architecture with a Reinforcement Learning algorithm generating a computer program capable of beating classic Atari video games without being explicitly programmed for this task, scoring as a top human player. It is not surprising, therefore, that they appear in the last Nature’s cover (vol. 518, Num. 7540, pp. 456-568) . But how do they work? First, one must take a close look to the hum