Large margin perceptrons for document categorization
AssigneeMicrosoft Corporation
InventorsHugo Zaragoza, Ralf Herbrich
Life of this patent
Estimating the term…
Abstract
A modified large margin perceptron learning algorithm (LMPLA) uses asymmetric margin variables for relevant training documents (i.e., referred to as “positive examples”) and non-relevant training documents (i.e., referred to as “negative examples”) to accommodate biased training sets. In addition, positive examples are initialized to force at least one update to the initial weighting vector. A noise parameter is also introduced to force convergence of the algorithm.
Claims16
One or more computer-readable media having executable instructions stored thereon that, when executed, implement a method comprising:
- receiving a plurality of training documents;
- for each training document:
- determining if the training document is a relevant document or a non-relevant document;
- if the training document is a relevant training document, determining whether the relevant training document introduces an error within a first margin relative to an inner product associated with the relevant training document;
- if the training document is a non-relevant training document,
- determining whether the non-relevant training document introduces an error within a second margin relative to an inner product associated with the non-relevant training document;
- updating a classification weighting vector based on a document vector of the training document, if the training document introduces an error;
- and
- storing the updated classification weighting vector.
A method comprising:
- receiving a set of training documents;
- if a training document is a relevant training document and the training document introduces an error within a first margin relative to an inner product associated with the training document, updating a classification weighting vector based on a document vector of the training document;
- if a training document is a non-relevant training document and the training document introduces an error within a second margin relative to an inner product associated with the training document, updating the classification weighting vector based on a document vector of the training document;
- and
- storing the updated classification weighting vector.
A classification system for computing a classification weighting vector associated with a given category from a training set of training documents, the system comprising:
- a training module generating document vectors from the training set and generating the classification weighting vector based at least two asymmetric margins;
- and
- wherein the at least two asymmetric margins further comprise a first margin that is applied to relevant training documents and a second margin that is applied to non-relevant training documents.
One or more computer-readable media having executable instructions stored thereon that, when executed, implement a method comprising:
- initializing a classification weighting vector based on one or more document vectors of relevant training documents;
- determining whether a relevant training document introduces an error within a first margin relative to a perturbed inner product associated with the relevant training document;
- determining whether a non-relevant training document introduces an error within a second margin relative to a perturbed inner product associated with the non-relevant training document;
- and
- for each training document that introduces an error, updating the classification weighting vector based on a document vector of the training document.
A method comprising:
- receiving a set of training documents;
- initializing a classification weighting vector based on one or more document vectors of relevant training documents from the training set;
- for each training document in the training set:
- determining if the training document is a relevant training document or a non-relevant training document;
- if the training document is a relevant training document, determining whether the relevant training document introduces an error within a first margin relative to a perturbed inner product associated with the relevant training document;
- if the training document is a non-relevant training document, determining whether the non-relevant training document introduces an error within a second margin relative to a perturbed inner product associated with the non-relevant training document;
- and
- if the training document introduces an error relative to the first or second margin, updating the classification weighting vector based on a document vector of the training document.
A classification system for computing a classification weighting vector associated with a given category from a training set of training documents, the system comprising:
- a training module configured to:
- initialize the classification weighting vector based on one or more document vectors of relevant training documents from the training set;
- for each training document:
- determine, if the training document introduces an error within a margin relative to a perturbed inner product associated with the training document;
- updating the classification weighting vector based on a document vector of the training document if the training document introduces an error within the margin;
- and
- wherein the margin is a first margin if the training document is a relevant training document, the margin is a second margin if the training document is a non-relevant training document, and the first margin is not equal to the second margin.
Dependencies are inferred from the claim text.