EXPONENTIAL PRIORS FOR MAXIMUM ENTROPY MODELS
First Claim
1. A machine implemented system that facilitates maximizing probabilities comprising:
- a data input component that provides one or more types of data for analysis; and
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 and at least one of;
an Exponential prior, a LaPlacian prior, or a non-Gaussian distribution and an alternative iterative scaling function.
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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.
15 Citations
20 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; and
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 and at least one of;
an Exponential prior, a LaPlacian prior, or a non-Gaussian distribution and an alternative iterative scaling function. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8)
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9. 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. - View Dependent Claims (10, 11, 12, 13, 14, 15, 16, 17, 18, 19)
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20. A computer-readable medium having stored thereon the following computer executable components:
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a data input component that provides one or more types of data for analysis; and
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 and at least one Exponential prior, a LaPlacian prior, or a non-Gaussian distribution.
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Specification