Something Different

-
Endless meanders in rivers of silver,
synthetic in nature, makes us shiver.
As old mathematicians long ago gone
you keep counting alone.
Now that the world is at your feet
your eager beat is only for an elite.

Many dreams of fame
ephemeral thoughts, now a shame.
I have taken you in adoption
without considering any other option.
In our sweet loneliness
we see time passing by; tireless mess.

Comments

Post a Comment

Popular posts from this blog

Data Streams and VFML

-
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...
Read more

Toward ensemble methods: A primer with Random Forest

-
The Kaggle-Higgs competition has attracted my attention very much lately. In this particular challenge, the goal is to generate a predictive model out of a bunch of data taken from distinct LHC’s detectors. The numbers are the following: 250000 properly labeled instances, 30 real-valued features (containing missing values), and two classes, namely background { b } and signal { s }. Also, the site provides a test set consisting of 550000 unlabeled instances. There are nearly 1300 participants while I am writing this post, and a lot of distinct methods are put to the test to win the challenge. Very interestingly, ensemble methods are all in the head of the leaderboard, and the surprise is XGBoost , a gradient boosting method that makes use of binary trees.   After checking the computational horsepower of the XGBoost algorithm by myself, I decided to take a closer look at ensemble methods. To start with, I implemented a Random Forest , an algorithm that consists of many indep...
Read more

Optimization: Simplex Algorithm

-
Imagine we have a set of computers running simulations. Those computers can be described by a group of features, for example its power consumption per hour and the total number of processes it can run per hour. From our R+D personnel, a series of programs are send to these computers so we have to perform some schedule to run the applications, but for our profit we wish to minimise the total consumption of the set of machines. Let's show some more detail: Computer 1 has a power consumption of 100 watts per hour and can run 10  high cpu-demanding processes per hour and 7 low cpu-demanding processes per hour at the same time. Computer 2 has a power consumption of 350 watts per hour and can run 60 high cpu-demanding processes per hour and 20 low cpu-demanding processes per hour at the same time. From the R+D area there are a set of at least 3125 high cpu-demanding processes and at least 710 low cpu-demanding processes to be processed. How many hours should o...
Read more