Data classification using stochastic key feature generation
First Claim
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1. A computer implemented method comprising:
- obtaining a set of training data having associated summaries;
using the set of training data and associated summaries to generate a key feature generation model;
obtaining another set of training data having associated categories;
mapping, using the key feature generation model, the other set of training data to a set of vectors; and
training a data classifier based on the set of vectors and the associated categories; and
classifying data using the trained data classifier.
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Abstract
Data classification using stochastic key feature generation includes obtaining a set of training data having associated summaries. The set of training data and associated summaries are used to generate a key feature generation model. Another set of training data having associated categories is also obtained, and the key feature generation model is used to map this other set of training data to a set of vectors. A data classifier is then trained based on the set of vectors and the associated categories.
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Citations
44 Claims
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1. A computer implemented method comprising:
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obtaining a set of training data having associated summaries; using the set of training data and associated summaries to generate a key feature generation model; obtaining another set of training data having associated categories; mapping, using the key feature generation model, the other set of training data to a set of vectors; and training a data classifier based on the set of vectors and the associated categories; and classifying data using the trained data classifier. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17)
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18. One or more computer readable media having stored thereon a plurality of instructions that, when executed by one or more processors of a device, causes the one or more processors to:
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obtain a set of training text having associated summaries; use the set of training text and associated summaries to generate a keyword generation model; obtain another set of training text having associated categories; map, using the keyword generation model, the other set of training text to a set of vectors; and train a text classifier based on the set of vectors and the associated categories. - View Dependent Claims (19, 20)
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21. A computer-implemented method of classifying data, the method comprising:
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receiving data to be classified; using a key feature generation model to obtain a vector representing the data, wherein the key feature generation model is based on a set of training data having associated summaries; and inputting the obtained vector to a trained data classifier, wherein the trained data classifier was previously trained using the set of training data and associated summaries. - View Dependent Claims (22, 23, 24)
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25. One or more computer readable media having stored thereon a plurality of instructions that, when executed by one or more processors of a device, causes the one or more processors to:
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train a text classifier using multiple pieces of training text a plurality of summaries wherein each of the plurality of summaries is associated with one of the multiple pieces of training text, and a plurality of categories wherein each of the plurality of categories is associated with one of the multiple pieces of training text; and use the trained text classifier to classify input text without an associated summary. - View Dependent Claims (26, 27, 28, 29)
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30. A system comprising:
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a stochastic key feature generation model training module to generate a trained model based on a first training set wherein the first training set includes training data and associated summaries; a training data mapping module to generate a plurality of vectors based on the trained model and a second training set, wherein the second training set includes training data and associated categories; and a classifier training module to construct a trained classifier based on the plurality of vectors and the second training set. - View Dependent Claims (31, 32, 33, 34, 35, 36, 37, 38, 39)
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40. A system comprising:
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a stochastic key feature generation model-based vector generation module to generate a vector based on input data and a stochastic key feature generation model, wherein the stochastic key feature generation model was previously generated based on training data and associated summaries; and a classifier to receive the vector and, based on the vector, classify the input data into one or more classes. - View Dependent Claims (41, 42)
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43. A system comprising:
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means for generating a trained model based on a first training set, wherein the first training set includes training data and associated summaries; means for generating a plurality of vectors based on the trained model and a second training set) wherein the second training set includes training data and associated categories; and means for constructing a trained classifier based on the plurality of vectors and the second training set. - View Dependent Claims (44)
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Specification