Exponential priors for maximum entropy models
First Claim
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1. A machine implemented system that facilitates maximizing probabilities comprising:
- a data input component that provides one or more types of data for analysis;
an analysis component that analyzes at least a subset of the one or more types of data to compute maximized probabilities by employing an improved iterative scaling function, a plurality of Exponential priors that respectively correspond to a plurality of different features, and at least one of a LaPlacian prior, a non-Gaussian distribution, or an alternative iterative scaling function;
Anda training component that computes one or more weights for respective test data based at least in part on the maximized probabilities.
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Abstract
The subject invention provides for systems and methods that facilitate optimizing one or mores sets of training data by utilizing an Exponential distribution as the prior on one or more parameters in connection with a maximum entropy (maxent) model to mitigate overfitting. Maxent is also known as logistic regression. More specifically, the systems and methods can facilitate optimizing probabilities that are assigned to the training data for later use in machine learning processes, for example. In practice, training data can be assigned their respective weights and then a probability distribution can be assigned to those weights.
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Citations
18 Claims
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1. A machine implemented system that facilitates maximizing probabilities comprising:
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a data input component that provides one or more types of data for analysis; an analysis component that analyzes at least a subset of the one or more types of data to compute maximized probabilities by employing an improved iterative scaling function, a plurality of Exponential priors that respectively correspond to a plurality of different features, and at least one of a LaPlacian prior, a non-Gaussian distribution, or an alternative iterative scaling function;
Anda training component that computes one or more weights for respective test data based at least in part on the maximized probabilities. - View Dependent Claims (2, 3, 4, 5, 6, 7)
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8. A method effectuated on a machine that facilitates maximizing probability values, comprising:
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receiving types of data for analysis; and analyzing a subset of the types of data to ascertain maximized probabilities by utilizing an improved iterative scaling function and employing a plurality of Exponential priors that are respectively associated with different features; and training a machine learning system at least in part by applying weights to respective test data based on the maximized probabilities. - View Dependent Claims (9, 10, 11, 12, 13, 14, 15, 16, 17)
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18. A system that facilitates maximizing probabilities, comprising:
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a memory for storing data; and a processor coupled to the memory configured to act as; a data input component that provides one or more types of data for analysis; an analysis component that analyzes at least a subset of the one or more types of data to compute maximized probabilities at least in part by employing an improved iterative scaling function, at least two Exponential priors corresponding to respective disparate features, and at least one of a LaPlacian prior or a non-Gaussian distribution; and a training component that trains a machine learning system based at least in part on the maximized probabilities.
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Specification