International Business Machines Corporation

Yahoo! Inc.

Tong Zhang

Frank J Oles

Rudolf Maarten Bolle

Norman C Haas

Byron Edward Dom

David E Johnson

Alexandrin Popescul

An improved system and method is provided for determining web page quality using collective inference based on local and global web page information. A classification engine may be provided for classifying a web page using local features of a seed set of web pages and global web graph information about the seed set of web pages. A dual algorithm based on graph regularization formulated as a well-formed optimization solution may be used in an embodiment for applying collective inference for binary classification of the web page using the local web page information and global web graph information of a web page, the local web page information and global web graph information of an authoritative set of web pages, and the local web page information and global web graph information of a non-authoritative set of web pages.

System and method for determining web page quality using collective inference based on local and global information

This invention is a business system and method to perform categorization (classification) of multimedia items and to make business decisions based on the categorization of the item. The multimedia items are comprised of a multitude of disparate information sources, in particular, visual information and textual information. Classifiers are induced based on combining textual and visual feature vectors. Textual features are the traditional ones. Visual features include, but are not limited to, color properties of key intervals and motion properties of key intervals. The visual feature vectors are determined in such a fashion that the vectors are sparse. The text and the visual representation vectors are combined in a systematic and coherent fashion. This vector representation of a media item lends itself to well-established learning techniques and can be used for multimedia item categorization. The resulting business system, subject of this invention, can be used for many purposes. An example here are enforcement of copyright, trademark, intellectual property, parental guidance and common decency restrictions. Other uses are multimedia item classifier to determine routing of incoming items or building user profiles based on user multimedia preferences.

Business method and apparatus for employing induced multimedia classifiers based on unified representation of features reflecting disparate modalities

This invention is a system and method to perform categorization (classification) of multimedia items. These items are comprised of a multitude of disparate information sources, in particular, visual information and textual information. Classifiers are induced based on combining textual and visual feature vectors. Textual features are the traditional ones, such as, word count vectors. Visual features include, but are not limited to, color properties of key intervals and motion properties of key intervals. The visual feature vectors are determined in such a fashion that the vectors are sparse. The vector components are features such as the absence or presence of the color green in spatial regions and the absence or the amount of visual flow in spatial regions of the media items. The text and the visual representation vectors are combined in a systematic and coherent fashion. This vector representation of a media item lends itself to well-established learning techniques. The resulting system, subject of this invention, categorizes (or classifies) media items based both on textual features and visual features.

Method and apparatus for inducing classifiers for multimedia based on unified representation of features reflecting disparate modalities

A method to automatically categorize messages or documents containing text. The method of solution fits in the general framework of supervised learning, in which a rule or rules for categorizing data is automatically constructed by a computer on the basis of training data that has been labeled beforehand. More specifically, the method involves the construction of a linear separator: training data is used to construct for each category a weight vector w and a threshold t, and the decision of whether a hitherto unseen document d is in the category will depend on the outcome of the test w x&gE;t, where x is a vector derived from the document d. The method also uses a set L of features selected from the training data in order to construct the numerical vector representation x of a document. The preferred method uses an algorithm based on Gauss-Seidel iteration to determine the weight factor w that is determined by a regularized convex optimization problem derived from the principle of minimizing modified training error.

Text categorizers based on regularizing adaptations of the problem of computing linear separators

A method to automatically categorize messages or documents containing text. The method of solution fits in the general framework of supervised learning, in which a rule or rules for categorizing data is automatically constructed by a computer on the basis of training data that has beforehand been categorized, i.e., each training data item has been labeled with the categories to which it belongs. More specifically, the method for rule induction involves the novel combination of (1) inducing from the training data a decision tree for each category, (2) automated construction from each decision tree of a simplified symbolic rule set that is logically equivalent overall to the decision tree, and which is to be used for categorization instead of the decision tree, and (3) determination of a confidence level for each rule. The method covers both decision-tree-based symbolic rule induction and the use for the purpose of document categorization of rules in the logical format of those generated by the rule induction procedure described herein.

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Tuckahoe, NY, United States of America

Tong Zhang