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We live in a technological world crowded of information. Every device we can think of can give us a bunch of such data, usually in the form of a flow or stream of information in, more or less, real time . In this particular situation classical knowledge discovery mechanisms (like our loved C4.5, a decision tree developed by Quinlan) are completely unable of extract a correct model of the situation. But, what is so special with flows of data? Following the words of Gama and Rodriques: a data stream is an ordered sequence of instances that can be read only once or a small number of times using limited computing and storage capabilities. These sources of data are characterized by being open-ended, following at high speed, and generated by non-stationary distributions in dynamic environments . So, to properly handle this kind of knowledge the learning algorithm has to learn on line and process massive amounts of data increasing the challenges to be faced. Let's hold one's breath w

$\LaTeX$ is a powerful tool to properly express our ideas and thoughts that every wannabe researcher should know. A question I had, sometime ago, were if $\LaTeX$ worked in Blogger. Today I have found the answer: yes, we can use $\LaTeX$ here: $y_{k} = \sum_{i}^{l} \alpha_{i} + 1$ The two things you have to do are: (1) create a new HTML/Javascript third-party application, and (2) enter the code found here . The script replaces the $\LaTeX$ code for images, easy and fast :-)

Today I want to share some thoughts on creativity and research. As a wannabe researcher, sometimes I get completely obscured by apparently impossible problems. How in the world this problem or that other one are solved? Why this solution/formula/algorithm worked and how? Sometimes straight, good-old logical thinking is not enough to confront problems and the only true way is to take the problem from another point of view, introducing fresh, radical and creative ideas. I want to share two wonderful videos that talk about creativity. The first one is take the world from another point of view, an interview to the great Richard Feynman and the second is a talk performed by Murray Gell-mann. Professor Feynman, Novel Price awarded in 1965, famous for his Feynman Diagrams among many other things is interviewed and talks about how to confront complex problems in the research world. Its a worth seeing video that no researcher has to miss . Professor Gell-mann, Novel Price awarded in 1969, famo

One can define machine learning (ML) as programs that learn and improve with the use of the experience at some task using a measure of the performance. We can classify ML techniques basing on the desired outcome of the algorithm. There are, in the classic taxonomy, three main types of learning: (1) supervised learning , where an expert provides feedback in the learning process, (2) unsupervised learning , where there is no teacher or expert when the learning process is running, and (3) reinforcement learning , where the program learns interacting with the environment. Of all these, we will talk about the first one. Supervised learning is a ML method for extracting a model from training data. These data consist of a set (called examples ) of input attributes (sometimes called features ) and the desired output. As commented before, the main characteristic of supervised learning is that the program needs an expert or teacher that provides feedback in the learning process. Typically, the

Over the past decades the study of machine learning (ML) has grown from the efforts of engineers exploring whether computers could learn to perform some tricky actions to a broad discipline. The purpose of this blog is to give my personal view and thoughts on this wonderful subject. Feel free to comment whatever you wish.