This is the second part of a simple and brief guide to the Random Forest algorithm and its implementation in R. If you missed Part I, you can find it here. randomForest in R R has a package called randomForest which contains a randomForest function. If you want to explore in depth this implementation, I […]
Category: inverse problems
A small guide to Random Forest - part 1
I've recently started playing with Kaggle and got curious about one of the most famous classification/regression framework, Random Forest. In a problem of classification or regression, several random decision trees (a "forest") are built and at the end the outputs are combined ("bagging"). The intuition is that randomness and a meaningful quantity of trees will avoid […]
Open Data: CT datasets and prototypes
In my research work, I often find it difficult to get datasets for X-ray CT for method validation, neither simulated and real data. Of course, there's the classic Shepp-Logan phantom, but in many cases it would save a lot of work to download datasets to test one's methods. As for my knowledge, there is no broad […]
Science Slam Helsinki 2015: a triumph for Inverse Problems
Last November I attended a fun event here in Helsinki: a sort of mix between science and stand-up comedy called Science Slam Helsinki. A Science Slam is a form of science communication to general audience. Each scientist gives a short talk (10-15 mins) to popularise science. Often such events are held in non-academic places, like pubs. Science […]
Live from Inverse Days 2015: baby on board
This week I did something a little crazy, dictated by necessity: I took my 2 year old girl to a conference, namely the Inverse Days in Lappeenranta. We drove from Espoo (bad idea) on Monday evening and will stay until Thursday, cutting at half day to get home not too late. Baby-wise it went much […]
Coding coding coding
Despite the confusing title, in the past days I have been coding. A lot. Even though I am dying to leak information about what exactly I am working on, I still need to wait few weeks to reveal. I thought anyway to write something about programming from the perspective of an applied mathematician. Research forces you […]
Live from Münster: summer school on inverse problems 2015
I have been visiting the lovely German town of Münster in the past two weeks and in the last few days I have been attending a summer school on inverse problems here. Even though knowing the family was struggling at home to keep up the routine has been worrying me, I must say I enjoyed these days here, both […]
Tweeting for Real Scientists: aftermath.
My week as curator of the Twitter channel @realscientists has just ended. It was refreshing and a lot of fun. I had the chance to review my own work from a fresh perspective and to check out old problems I didn't read about for a while. For those who don't know what Real Scientists is, I […]
4D tomography: walkthrough of my project - part 3
Here comes the final part of the walkthrough of my current project on dynamic sparse tomography (see also part 1 and part 2). In the previous post I left the question of the choice of the cut-off function hanging. In a classical level set method, would be the Heaviside step function. The Heaviside function is […]
4D tomography: walkthrough of my project - part 2
After talking about motivation (see the first part and then part 3), I will now go into details with the mathematics foundations of the project. The novel tomography reconstruction algorithm I am contributing developing is based on a level set method approach. Level set methods A level set method is an elaborate, yet geometrically intuitive, framework to deal […]