APR course October 2010
The Advanced Pattern Recognition course just ended last week. 
The course was fully booked with participants really coming from all around the world… from USA to Indonesia! We enjoyed meeting them and discussing all sort of pattern recognition issues, often till later in the evening, even on Friday! The background has been very variate, ranging from participants with PhD in this field,to persons new to pattern recognition. Yet, we were happy to hear that all learned relevant things, whether a broad view of methods, a specific topic of their interest, or a practical clue on how and why to use a method.
Understanding outliers with interactive scatter
How to choose a classifier? How to train my detection system? Yes, we do have a set of classifiers at our disposal, and we can "try" different approaches. But, in the end, our choice or direction is driven by our intuition. To develop an intuitive feeling of what might work for our problem, it is important to understand our data.
Interactive visualization tools come to our aid in building this understanding. The sdscatter of PRSD Studio helps us to visualize different sets of labels, focus only on the classes of interest, connect the sample in the feature space with its original image.
In this short video we show how simple interactive tools help us understand what causes the outliers and how we can easily remove them.
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ICPR 2010
This year the International Conference on Pattern Recognition (ICPR) was in Istanbul, Turkey. 
The conference location was very nice and the organization perfect. We really enjoyed meeting old and new colleagues. With several parallel tracks, there was plenty of research ideas to learn and discuss. Many people were interested in our poster on optimization of classifier hierarchies. In fact, we had so many visitors to our poster that had no time to walk around the other posters of the session!
Of course we also enjoyed the warm atmosphere of Istanbul, with its historical buildings, lively bazaars and delicious food!
(0) CommentsVisualization of classifier decisions in multi-D spaces
Visualization of classifier decisions in a feature space help us to understand its behavior. The visualization is straightforward when our data has only two features. But what about multi dimensional problems?
PRSD Studio provides visualization of classifier decisions in feature spaces with more than two dimensions.
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Tutorial example on protecting classifier from outliers
Often, we need to protect a trained classifier from accepting outliers examples appearing in production. This tutorial shows how to achieve this by addding a rejection option to a trained discriminant with sdreject command. Construction of interactive reject curve is also illustrated.
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Detailed example on adding reject option is available in the Knowledge Base
(0) CommentsASCI course 2010
Last week, we lectured in the Advanced Pattern Recognition Course organized by TU Delft within the Advanced School for Computing and Imaging (ASCI school). 
The course is offered to PhD students that are interested in the field of Pattern Recognition or that are already experienced and would like to deepen and widen their understanding of the field. This course has been more then fully booked, with 30 participants from all corners of The Netherlands. Our lectures have focused on evaluation, ROC analysis and classifier optimization, leading to an integrated approach for system design. It has been a pleasure for us to meet so many bright students and learn about their interesting projects. We wish them success and fun in their research!
Tutorial example on optimizing three-class classifiers with ROC analysis
Sometimes, one of the classes in multi-class problem is much larger than the remaining classes. Classifiers, trained in such imbalanced problem, usually deliver very poor performances with a default decision function. The reason is that the model output of the large class dominates the solution. The default procedure of making decisions assumes that all the classes are equally important which results in high misclassification of small classes.
PRSD Studio allows you to quickly optimize multi-class classifiers in imbalanced problems. Watch the video inside!
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How to quickly rename classes or define meta-classes?
When designing classifiers, we often need to define new classes by renaming existing ones. sdrelab command allows us to do just that quickly and easily.
Class relabeling helps us to define meta-classes, to compare data sets before and after normalization or to understand where the data of a specific sub-class/patient/cluster fits with respect to others.
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Presenting our research on ROC hierachical classifiers at NVPHBV meeting
We have presented our research on optimization of hierarchical classifiers at the spring meeting of NVPHBV (Dutch society for pattern recognition and image processing).
Complex problems are often easier to handle if decomposed into sub-problems and tackled independently. Hierarchical classifiers offer a great tool for such decomposition but are difficult to optimize according to application requirements. This is a serious problem we encounter daily in our industrial projects. In our talk, we described our approach allowing the designer to perform cost-sensitive ROC optimization for apriori-defined hierarchical classifiers.
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How to setup leave-one-patient out cross-validation
In many applications, we need to make sure our classifier generalizes to unseen patients, object events etc. Therefore, we need to consider these entities in cross-validation of our algorithm. PRSD Studio provides leave-one-object-out using the sdcrossval routine. But in this example, we show how to make a very simple leave-one-object-out scheme in two lines of code where everything is open to our direct understanding.
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