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Pattern recognition methods

    A. Applications in which the training data comprises examples of the input vectors along with their corresponding target vectors are known as supervised learning problems.


         1. Cases such as the digit recognition example, in which the aim is to assign each input vector to          one of a finite number of discrete categories, are called classification problems.

         2. If the desired output consists of one or more continuous variables, then the task is called                regression

    B. In other pattern recognition problems, the training data consists of a set of input vectors x without any corresponding target values. The goal in such unsupervised learning problems may be  

         1. To discover groups of similar examples within the data, where it is called clustering:

         2. To determine the distribution of data within the input space, known as density estimation,

         3. to project the data from a high-dimensional space down to two or three dimensions for the              purpose of visualization.

    C. Finally, the technique of reinforcement learning (Sutton and Barto, 1998) is concerned
    with the problem of finding suitable actions to take in a given situation in
    order to maximize a reward. Here the learning algorithm is not given examples of
    optimal outputs, in contrast to supervised learning, but must instead discover them
    by a process of trial and error.


    Bishop, C. M. [2006] Pattern recognition and machine learning. Springer, p. 3.

     


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    Pattern recognition methods

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