N-tuple or RAM based neural network classification system and method
First Claim
1. A method of training a computer classification system which can be defined by a network comprising a number of n-tuples or Look Up Tables (LUTs), with each n-tuple or LUT comprising a number of rows corresponding to at least a subset of possible classes and further comprising a number of columns being addressed by signals or elements of sampled training input data examples, each column being defined by a vector having cells with values, said method comprising determining the column vector cell values based on one or more training sets of input data examples for different classes so that at least part of the cells comprise or point to information based on the number of times the corresponding cell address is sampled from one or more sets of training input examples, and determining weight cell values corresponding to one or more column vector cells being addressed or sampled by the training examples to thereby allow weighting of one or more column vectors cells of positive value during a classification process, said weight cell values being determined based on the information of at least part of the determined column vector cell values and by use of at least part of the training set(s) of input examples.
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Abstract
A method and system for training a computer classification system which can be defined by a network of a number of n-tuples or Look Up Tables (LUTs), with each n-tuple or LUT including a number of rows corresponding to at least a subset of possible classes and further including a number of columns being addressed by signals or elements of sampled training input data examples, each column being defined by a vector having cells with values, wherein the column vector cell values are determined based on one or more training sets of input data examples for different classes so that at least part of the cells comprise or point to information based on the number of times the corresponding cell address is sample from one or more sets of training input examples, and weight cell values are determined, corresponding to one or more column vector cells being addressed or sampled by the training examples.
39 Citations
49 Claims
- 1. A method of training a computer classification system which can be defined by a network comprising a number of n-tuples or Look Up Tables (LUTs), with each n-tuple or LUT comprising a number of rows corresponding to at least a subset of possible classes and further comprising a number of columns being addressed by signals or elements of sampled training input data examples, each column being defined by a vector having cells with values, said method comprising determining the column vector cell values based on one or more training sets of input data examples for different classes so that at least part of the cells comprise or point to information based on the number of times the corresponding cell address is sampled from one or more sets of training input examples, and determining weight cell values corresponding to one or more column vector cells being addressed or sampled by the training examples to thereby allow weighting of one or more column vectors cells of positive value during a classification process, said weight cell values being determined based on the information of at least part of the determined column vector cell values and by use of at least part of the training set(s) of input examples.
- 23. A method of training a computer classification system which can be defined by a network comprising a number of n-tuples or Look Up Tables (LUTs), with each n-tuple or LUT comprising a number of rows corresponding to at least a subset of possible classes and further comprising a number of columns being addressed by signals or elements of sampled training input data examples, each column being defined by a vector having cells with values, said method comprising determining the column vector cell values based on one or more training sets of input data examples for different classes so that at least part of the cells comprise or point to information based on the number of times the corresponding cell address is sampled from one or more sets of training input examples, and determining weight cell values corresponding to at least a subset of the column vector cells to thereby allow boosting of one or more column vector cells during a classification process, said weight cell values being determined based on the information of at least part of the determined column vector cell values and by use of at least part of the training set(s) of input examples.
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25. A method of determining weight cell values in a computer classification system which can be defined by a network comprising a number of n-tuples or Look Up Tables (LUTs), with each n-tuple or LUT comprising a number of rows corresponding to at least a subset of possible classes and further comprising a number of column vectors with at least part of said column vectors having corresponding weight vectors, each column vector being addressed by signals or elements of a sampled training input data example and each column vector and weight vector having cells with values being determined based on one or more training sets of input data examples for different classes, said method comprising determining the column vector cell values based on the training set(s) of input examples so that at least part of said values comprise or point to information based on the number of times the corresponding cell address is sampled from the set(s) of training input examples, and determining weight vector cell values corresponding to one or more column vector cells based on the information of at least part of the determined column vector cell values and by use of at least part of the training set(s) of input examples, said determination allowing weighting of column vector cells having a positive value (greater than 0) and column vector cells having a non-positive value (lesser than or equal to 0).
- 26. A system for training a computer classification system which can be defined by a network comprising a stored number of n-tuples or Look Up Tables (LUTs), with each n-tuple or LUT comprising a number of rows corresponding to at least a subset of possible classes and further comprising a number of columns being addressed by signals or elements of sampled training input data examples, each column being defined by a vector having cells with values, said system comprising input means for receiving training input data examples of known classes, means for sampling the received input data examples and addressing column vectors in the stored set of n-tuples or LUTs, means for addressing specific rows in the set of n-tuples or LUTs, said rows corresponding to a known class, storage means for storing determined n-tuples or LUTs, means for determining column vector cell values so as to comprise or point to information based on the number of times the corresponding cell address is sampled from the training set(s) of input examples, and means for determining weight cell values corresponding to one or more column vector cells being addressed or sampled by the training examples to thereby allow weighting of one or more column vectors cells of positive value during a classification process, said weight cell values being determined based on the information of at least part of the determined column vector cell values and by use of at least part of the training set(s) of input examples.
- 41. A system for training a computer classification system which can be defined by a network comprising a stored number of n-tuples or Look Up Tables (LUTs), with each n-tuple or LUT comprising a number of rows corresponding to at least a subset of possible classes and further comprising a number of columns being addressed by signals or elements of sampled training input data examples, each column being defined by a vector having cells with values, said system comprising input means for receiving training input data examples of known classes, means for sampling the received input data examples and addressing column vectors in the stored set of n-tuples or LUTs, means for addressing specific rows in the set of n-tuples or LUTs, said rows corresponding to a known class, storage means for storing determined n-tuples or LUTs, means for determining column vector cell values so as to comprise or point to information based on the number of times the corresponding cell address is sampled from the training set(s) of input examples, and means for determining weight cell values corresponding to at least a subset of the column vector cells to thereby allow boosting of one or more column vectors cells during a classification process, said weight cell values being determined based on the information of at least part of the determined column vector cell values and by use of at least part of the training set(s) of input examples.
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43. A system for determining weight cell values of a classification network which can be defined by a stored number of n-tuples or Look Up Tables (LUTs), with each n-tuple or LUT comprising a number of rows corresponding to at least a subset of the number of possible classes and further comprising a number of column vectors with at least part of said column vectors having corresponding weight vectors, each column vector being addressed by signals or elements of a sampled training input data example and each column vector and weight vector having cell values being determined during a training process based on one or more sets of training input data examples, said system comprising:
- input means for receiving training input data examples of known classes, means for sampling the received input data examples and addressing column vectors and corresponding weight vectors in the stored set of n-tuples or LUTs, means for addressing specific rows in the set of n-tuples or LUTs, said rows corresponding to a known class, storage means for storing determined n-tuples or LUTs, means for determining column vector cell values so as to comprise or point to information based on the number of times the corresponding cell address is sampled from the training set(s) of input examples, and means for determining weight vector cell values corresponding to one or more column vector cells based on the information of at least part of the determined column vector cell values and by use of at least part of the training set(s) of input examples, said determination allowing weighting of one or more column vector cells having a positive value (greater than
0) and one or more column vector cells having a non-positive value (lesser than or equal to
0).
- input means for receiving training input data examples of known classes, means for sampling the received input data examples and addressing column vectors and corresponding weight vectors in the stored set of n-tuples or LUTs, means for addressing specific rows in the set of n-tuples or LUTs, said rows corresponding to a known class, storage means for storing determined n-tuples or LUTs, means for determining column vector cell values so as to comprise or point to information based on the number of times the corresponding cell address is sampled from the training set(s) of input examples, and means for determining weight vector cell values corresponding to one or more column vector cells based on the information of at least part of the determined column vector cell values and by use of at least part of the training set(s) of input examples, said determination allowing weighting of one or more column vector cells having a positive value (greater than
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44. A system for classifying input data examples into at least one of a plurality of classes, said system comprising:
- storage means for storing a number or set of n-tuples or Look Up Tables (LUTs) with each n-tuple or LUT comprising a number of rows corresponding to at least a subset of the number of possible classes and further comprising a number of column vectors with corresponding weight vectors, each column vector being addressed by signals or elements of a sampled input data example and each column vector and weight vector having cells with values being determined during a training process based on one or more sets of training input data examples, said system further comprising;
input means for receiving an input data example to be classified, means for sampling the received input data example and addressing columns and corresponding weight vectors in the stored set of n-tuples or LUTs, means for addressing specific rows in the set of n-tuples or LUTs, said rows corresponding to a specific class, means for determining an output value as a function of addressed weight cells, and means for comparing calculated output values corresponding to all classes and selecting the class or classes having maximum output value. - View Dependent Claims (45, 46, 47, 48, 49)
- storage means for storing a number or set of n-tuples or Look Up Tables (LUTs) with each n-tuple or LUT comprising a number of rows corresponding to at least a subset of the number of possible classes and further comprising a number of column vectors with corresponding weight vectors, each column vector being addressed by signals or elements of a sampled input data example and each column vector and weight vector having cells with values being determined during a training process based on one or more sets of training input data examples, said system further comprising;
Specification