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@broken2014-09-25 15:23:46
Books
@broken, 2014-09-25 15:23:46

What are the best books on pattern recognition you know?

Recommend books on image recognition (the best), I am a developer, but I have never come across this before, only around the bush. Only google helps. We need books that allow you to dot the i's, with examples, preferably.
It is possible with links to amazon / ebay / ozon, etc.

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[email protected]><e, 2014-10-02
@barmaley_exe

What do you understand by images? Usually recognition and images are separate things. For the first, machine learning approaches are used, and the second depends on the subject area. If we are talking about pictures, then the second one is the area called computer vision.
On machine learning, you can read the following:
- The Elements of Statistical Learning (available for free)
- Pattern Recognition and Machine Learning
- An Introduction to Statistical Learning (they say the book is simpler)
The first two are replete with mathematical details (although mathematics is there at the level of the basics of mathematics, algebra and probability theory with statistics), the third seems to be simpler. Well, it seems that specific examples of the application of these approaches cannot be found there. This is due to the fact that books are, in fact, a collection of descriptions of how tools work, and what you do with them is up to you (compare with a hammer: any interesting example of using a hammer will be too long and detailed for instructions).
As for computer vision: it should be noted here that this is a large area in itself, actively using machine learning methods, but not limited to them. It is difficult for me to recommend anything here, because I am not interested in this area.
In general, the main task is always the transition from the real world to the field of mathematical models (where machine learning algorithms can already be applied). Here you will need to collect data, clear it, twist it in every possible way. Data scientists like to say that 20% is data collection, 80% is data manipulation, and the rest is ML itself.

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