Estimation of Probability Density using Signature Tables for Application to Pattern Recognition. AIM-198
- Thosar, Ravindra B.
- Signature table training method consists of cumulative evaluation of a
- function (such as a probability density) at pre-assigned co-ordinate
- values of input parameters to the table. The training is conditional:
- based on a binary valued "learning" input to a table which is compared to
- the label attached to each training sample. Interpretation of an unknown
- sample vector is then equivalent of a table lookup, i.e. extraction of the
- function value stored at the proper co-ordinates. Such a technique is
- very useful when a large number of samples must be interpreted as in the case
- of samples must be interpreted as in the case of speech recognition and the
- time required for the trainng as well as for the recognition is at a premium.
- However, this method is limited by prhibitive storage requirements, even for
- a moderate number of parameters, when their relative independence cannot be
- assumed. This report investigates the conditions under which the higher
- dimensional probability density function can be decomposed so that the
- density estimate is obtained by a hierarchy of signature tables with
- consequent reduction in the storage requirement. Practical utility of the
- theoretical results obtained in the report is demonstrated by a vowel
- recognition experiment.
- Stanford Artificial Intelligence Laboratory
- Memo (Stanford Artificial Intelligence Laboratory)
- Artificial intelligence
- Finding Aid
- Stanford Artificial Intelligence Laboratory Records (SC1041)
- Stanford University. Libraries. Department of Special Collections and University Archives
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