The role of densities in pattern classification

Is pattern recognition about statistics? Well, it depends. If you see as its target to understand how new knowledge can be gained by learning from examples the role of statistics may be disputable. Knowledge lives in the human mind. It is born in the marriage between observations and reasoning. If we follow this process consciously…

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Discovered by accident

Some discoveries are made by accident. The wrong road brought a beautiful view. An arbitrary book from the library gave a great, new insight. A procedure was suddenly understood in a discussion with colleagues during a poster session. In a physical experiment a failure in controlling the circumstances showed a surprising phenomenon. Children playing with…

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Cross-validation

A returning question by students and colleagues is how to define a proper cross-validation: How many folds? Do we need repeats? How to determine the significance?. Here are some considerations. Why cross-validation? Cross-validation is a procedure for obtaining an error estimate of trainable system like a classifier. The resulting estimate is specific for the training…

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Adaboost and the Random Fisher Combiner

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 the support vector machine reviving the applicability of kernels. Then, from the turn of the century the combining of classifiers became popular, with significant fruits like adaboost…

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Using the test set for training

Never use the test set for training. It is meant for independent validation of the training result. If it has been somewhere included in the training stage it is not independent anymore and the evaluation result will be positively biased. This has been my guideline for a long time. Some students were shocked when I…

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My classifier scores 50% error. How bad is that?

What error rates can we expect for a trained classifier? How good or bad is a 50% error? Well, if classes are separable, a zero-error classifier is possible. But a very bad classifier may assign every object to the wrong class. Generally, all errors between zero and one are possible: . Much more can be…

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If we want to learn a new concept we may ask for a definition. This might be good in mathematics, but in real life it is often better to get some examples. Let us, for instance, try to understand what despair means. The dictionary tells us that it means ‘loss of hope’. This is just…

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Aristotle and the ugly duckling theorem

We already discussed several times the significance of understanding the Platonic and Aristotelian ways of gaining knowledge. It can be of great help to researchers in the field of pattern recognition in the appreciation of contributions by others, in discussions with colleagues and in supervising students. This may hold for science in general, but it…

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Why is the nearest neighbor rule so good?

Just compare the the new observations with the ones stored in memory. Take the most similar one and use its label. What is wrong with that? It is simple, intuitive, implementation is straightforward (everybody will get the same result), there is no training involved and it has asymptotically a very nice guaranteed performance, the Cover…

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There is no best classifier

Every problem has its own best classifier. Every classifier has at least one dataset for which it is the best. So there is no end to pattern recognition research as long as there are problems that are at least slightly different from all other ones that have been studied so far. The reason for this…

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