…Like in most areas, pattern classification and machine learning have their hypes. In the early 90-s the neural networks awoke and enlarged the community significantly. This was followed by…
Adaboost and the Random Fisher Combiner
Monday, June 8th, 2015 at
11:48
PRTools History
Monday, November 26th, 2012 at
00:00
…when I was a student in the field of physical instrumentation. Around 1990 I decided to leave the design of parallel computers for image analysis and returned to my original…
Personal history on the dissimilarity representation
Monday, February 11th, 2013 at
00:00
…was done more convincingly by others. Josef Kittler proposed to work on the newly arising domain of combining classifiers. I liked this ideas because the combined classifiers can be understood…
Cross-validation
Wednesday, July 15th, 2015 at
15:13
…evaluation. Cross-validation offers a solution. A second application is the comparison of different training procedures. The performance itself is not of primary importance here, but the comparison of different procedures…
Surprisingly good results in flow-cytometry classification
Sunday, July 20th, 2014 at
17:31
…matrices we computed a learning curve. The objects were repeatedly split in a subset T for training and S for testing. The matrix D(T,T) was interpreted as |T| objects in…
Kernel-induced space versus the dissimilarity space
Monday, February 18th, 2013 at
00:00
…linear discriminant, but also for a linear feature reduction procedure such as the principal component analysis. These procedures can be kernelized. The great advantage of this is based on the…
PRTools: building blocks for pattern recognition
Monday, December 3rd, 2012 at
00:25
…a set of observations of new objects, CLASSIFIER is a variable pointing to a procedure specifying the handling of the data and labeld is a command that converts the classification…
What is new in PRTools
Monday, January 28th, 2013 at
00:00
…Verzakov Tree with some visualization tools. Neural networks still constitute an inspiring paradigm for classifier design. There is a strong relation with the field of combining classifiers in case the…
Is every pattern recognition problem a small sample size problem?
Monday, February 23rd, 2015 at
14:56
…and perhaps a set of complicated neural networks to have any success. We are dealing with a complex concept which cannot easily be learned from a few examples. So may…
Peaking summarized
Monday, April 29th, 2013 at
00:00
…Pattern recognition learns from examples. Thereby, generalization is needed. This can only be done if the objects, or at least the differences between pattern classes have a finite complexity….
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