What do we want to recognize? What are the objects to be classified? What are our examples? What will be our observations? What are the classes we want to distinguish? Are they human defined or are we searching for some truth that might be hidden in the observations but not clearly defined? Many choices to…
Archive for October, 2012
The recognition task
Monday, October 29th, 2012 at
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Distances and densities
Friday, October 26th, 2012 at
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What is the ground for classifying a new object if we just have some some examples? How to we determine a proper class given a training set? We may find an identical copy in the examples if we are lucky. If there are more identical copies and they belong to different classes we are in…
Features need statistics, as they reduce
Monday, October 22nd, 2012 at
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Is statistics needed for learning? Well, it depends. A definite answer will be postponed for the time being. Here a first step will be made based on the traditional representation used for pattern recognition: the feature space. Objects belonging to different pattern classes differ. Otherwise it would not be possible to distinguish these classes. May…
Machine learning and pattern recognition
Thursday, October 18th, 2012 at
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Are Machine Learning (ML) and Pattern Recognition (PR) refering to the same field? Elsewhere we discussed the difference between Artificial Intelligence (AI) and PR. We tried to characterize them in their origin as the Platonic and Aristotelian ways of gaining knowledge: starting from the concepts or starting from the observations. But where do we have…
Artificial Intelligence and Pattern Recognition
Monday, October 15th, 2012 at
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What is the difference between Artificial Intelligence (AI) and Pattern Recognition (PR)? Is one a subfield of the other or do they stand next to each other? Historically these two fields are strongly connected. Meetings in the 50’s and 60’s attracted researchers from both domains and for many the interest was so broad or not…
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