TRAINING A HIDDEN MARKOV MODEL
First Claim
1. A computer program product comprising a non-transitory computer readable storage medium retaining program instructions, which program instructions when read by a processor, cause the processor to perform the steps of:
- obtaining a set of samples and labels thereof;
applying a Hidden Markov Model (HMM)-based classifier on the set of samples to obtain a set of predicted labels, whereby providing an initial prediction, wherein the HMM-based classifier is configured to utilize an HMM to predict a label for a sample, wherein the HMM is trained based on a training set;
computing a first F1-score of the initial prediction, wherein the first F1-score measures an accuracy of the initial prediction by comparing the predicted labels and the labels of the set of samples;
selecting a misclassified sample from the set of samples, wherein the misclassified sample is a sample that is misclassified by the HMM-based classifier in the initial prediction;
adding the misclassified sample to the training set;
in response to said adding, training the HMM based on the training set, whereby providing a modified HMM;
applying the HMM-based classifier using the modified HMM on the set of samples to obtain a second set of predicted labels, whereby providing a second prediction;
computing a second F1-score of the second prediction; and
comparing the first F1-score and the second F1-score, wherein in response to a determination that the first F1-score is greater than the second F1-score, removing the misclassified sample from the training set.
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Accused Products
Abstract
A computer program product, an apparatus and a method for training of an HMM. The method comprises applying a classifier that uses an HMM which was trained based on a training set, on a set of samples to provide an initial prediction; computing a first F1-score of the initial prediction measuring an accuracy of the initial prediction; selecting a misclassified sample by the classifier in the initial prediction; adding the misclassified sample to the training set; training the HMM using the misclassified sample to provide a modified HMM; applying the classifier using the modified HMM on the set of samples to provide a second prediction; computing a second F1-score of the second prediction; and comparing the first F1-score and the second F1-score; in response to a determination that the first F1-score is greater than the second F1-score, removing the misclassified sample from the training set.
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Citations
20 Claims
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1. A computer program product comprising a non-transitory computer readable storage medium retaining program instructions, which program instructions when read by a processor, cause the processor to perform the steps of:
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obtaining a set of samples and labels thereof; applying a Hidden Markov Model (HMM)-based classifier on the set of samples to obtain a set of predicted labels, whereby providing an initial prediction, wherein the HMM-based classifier is configured to utilize an HMM to predict a label for a sample, wherein the HMM is trained based on a training set; computing a first F1-score of the initial prediction, wherein the first F1-score measures an accuracy of the initial prediction by comparing the predicted labels and the labels of the set of samples; selecting a misclassified sample from the set of samples, wherein the misclassified sample is a sample that is misclassified by the HMM-based classifier in the initial prediction; adding the misclassified sample to the training set; in response to said adding, training the HMM based on the training set, whereby providing a modified HMM; applying the HMM-based classifier using the modified HMM on the set of samples to obtain a second set of predicted labels, whereby providing a second prediction; computing a second F1-score of the second prediction; and comparing the first F1-score and the second F1-score, wherein in response to a determination that the first F1-score is greater than the second F1-score, removing the misclassified sample from the training set. - View Dependent Claims (2, 3, 4, 5, 6, 7)
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8. A computer implemented method comprising:
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obtaining a set of samples and labels thereof; applying a Hidden Markov Model (HMM)-based classifier on the set of samples to obtain a set of predicted labels, whereby providing an initial prediction, wherein the HMM-based classifier is configured to utilize an HMM to predict a label for a sample, wherein the HMM is trained based on a training set; computing a first F1-score of the initial prediction, wherein the first F1-score measures an accuracy of the initial prediction by comparing the predicted labels and the labels of the set of samples; selecting a misclassified sample from the set of samples, wherein the misclassified sample is a sample that is misclassified by the HMM-based classifier in the initial prediction; adding the misclassified sample to the training set; in response to said adding, training the HMM based on the training set, whereby providing a modified HMM; applying the HMM-based classifier using the modified HMM on the set of samples to obtain a second set of predicted labels, whereby providing a second prediction; computing a second F1-score of the second prediction; and comparing the first F1-score and the second F1-score, wherein in response to a determination that the first F1-score is greater than the second F1-score, removing the misclassified sample from the training set. - View Dependent Claims (9, 10, 11, 12, 13, 14)
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15. A computerized apparatus having a processor, the processor being adapted to perform the steps of:
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obtaining a set of samples and labels thereof; applying a Hidden Markov Model (HMM)-based classifier on the set of samples to obtain a set of predicted labels, whereby providing an initial prediction, wherein the HMM-based classifier is configured to utilize an HMM to predict a label for a sample, wherein the HMM is trained based on a training set; computing a first F1-score of the initial prediction, wherein the first F1-score measures an accuracy of the initial prediction by comparing the predicted labels and the labels of the set of samples; selecting a misclassified sample from the set of samples, wherein the misclassified sample is a sample that is misclassified by the HMM-based classifier in the initial prediction; adding the misclassified sample to the training set; in response to said adding, training the HMM based on the training set, whereby providing a modified HMM; applying the HMM-based classifier using the modified HMM on the set of samples to obtain a second set of predicted labels, whereby providing a second prediction; computing a second F1-score of the second prediction; and comparing the first F1-score and the second F1-score, wherein in response to a determination that the first F1-score is greater than the second F1-score, removing the misclassified sample from the training set. - View Dependent Claims (16, 17, 18, 19, 20)
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Specification