Patent application number | Description | Published |
20090112691 | System and method for scheduling online keyword auctions over multiple time periods subject to budget and query volume constraints - An improved system and method for scheduling online keyword auctions over multiple time periods subject to budget constraints is provided. A linear programming model of slates of advertisements may be created for predicting the volume and order in which queries may appear throughout multiple time periods for use in allocating bidders to auctions to optimize revenue of an auctioneer. Each slate of advertisements may represent a candidate set of advertisements in order of optimal revenue to an auctioneer. Linear programming using column generation with the keyword as a constraint and a bidder's budget as a constraint may be applied for each time period to generate a column that may be added to a linear programming model of slates of advertisements. Upon receiving a query request, a slate of advertisements for the time period may be output for sending to a web browser for display. | 04-30-2009 |
20090150126 | System and method for sparse gaussian process regression using predictive measures - An improved system and method is provided for sparse Gaussian process regression using predictive measures. A Gaussian process regressor model may be construction by interleaving basis vector set selection and hyper-parameter optimization until the chosen predictive measure stabilizes. One of various LOO-CV based predictive measures may be used to find an optimal set of active basis vectors for building a sparse Gaussian process regression model by sequentially adding basis vectors selected using a chosen predictive measure. In a given iteration, a predictive measure is computed for each of the basis vectors in a candidate set of basis vectors and the basis vector with the best predictive measure is selected. The iterative addition of basis vectors may stop when predictive performance of the model degrades or no significant performance improvement is seen. | 06-11-2009 |
20090150309 | System and method for training a multi-class support vector machine to select a common subset of features for classifying objects - An improved system and method is provided for training a multi-class support vector machine to select a common subset of features for classifying objects. A multi-class support vector machine generator may be provided for learning classification functions to classify sets of objects into classes and may include a sparse support vector machine modeling engine for training a multi-class support vector machine using scaling factors by simultaneously selecting a common subset of features iteratively for all classes from sets of features representing each of the classes. An objective function using scaling factors to ensure sparsity of features may be iteratively minimized, and features may be retained and added until a small set of features stabilizes. Alternatively, a common subset of features may be found by iteratively removing at least one feature simultaneously for all classes from an active set of features initialized to represent the entire set of training features. | 06-11-2009 |
20090157578 | SYSTEM AND METHOD FOR GENERATING A CLASSIFIER MODEL - Generally, the present invention provides a method and computerized system for generating a classifier model, wherein the classifier model is operative to classify web content. The method and computerized system includes a first step of defining a plurality of predictive performance measures based on a leave one out (LOO) cross validation in terms of selectable model parameters. Exemplary predictive performance measures includes smoothened predictive measures such as F-measure, weighted error rate measure, area under curve measure, by way of example. The method and computerized system further includes deriving efficient analytical expressions for predictive performance measures to compute the LOO predictive performance and their derivatives. The next step is thereupon selecting a classifier model based on the LOO predictive performance. | 06-18-2009 |
20090274376 | METHOD FOR EFFICIENTLY BUILDING COMPACT MODELS FOR LARGE MULTI-CLASS TEXT CLASSIFICATION - A method of classifying documents includes: specifying multiple documents and classes, wherein each document includes a plurality of features and each document corresponds to one of the classes; determining reduced document vectors for the classes from the documents, wherein the reduced document vectors include features that satisfy threshold conditions corresponding to the classes; determining reduced weight vectors for relating the documents to the classes by comparing combinations of the reduced weight vectors and the reduced document vectors and separating the corresponding classes; and saving one or more values for the reduced weight vectors and the classes. Specific embodiments are directed to formulations for determining the reduced weight vectors including one-versus-rest classifiers, maximum entropy classifiers, and direct multiclass Support Vector Machines. | 11-05-2009 |
20100161527 | EFFICIENTLY BUILDING COMPACT MODELS FOR LARGE TAXONOMY TEXT CLASSIFICATION - A taxonomy model is determined with a reduced number of weights. For example, the taxonomy model is a tangible representation of a hierarchy of nodes that represents a hierarchy of classes that, when labeled with a representation of a combination of weights, is usable to classify documents having known features but unknown class. For each node of the taxonomy, the training example documents are processed to determine the features for which there are a sufficient number of training example documents having a class label corresponding to at least one of the leaf nodes of a subtree having that node as a root node. For each node of the taxonomy, a sparse weight vector is determined for that node, including setting zero weights, for that node, those features determined to not appear at least a minimum number of times in a given set of leaf nodes in the sub-tree with that node as a root node. The sparse weight vectors can be learned by solving an optimization problem using a maximum entropy classifier, or a large margin classifier with a sequential dual method (SDM) with margin or slack resealing. The determined sparse weight vectors are tangibly embodied in a computer-readable medium in association with the tangible representation of the nodes of the taxonomy. | 06-24-2010 |
20100161534 | PREDICTIVE GAUSSIAN PROCESS CLASSIFICATION WITH REDUCED COMPLEXITY - A computer-implemented method of generating a model of a sparse GP classifier includes performing basis vector selection and adding a thus-selected basis vector to a basis vector set, including performing a margin-based method that accounts for predictive mean and variance associated with all the candidate basis vectors at that iteration. Hyperparameter optimization is performed. The basis vector selection step and hyperparameter optimization step are such that the steps are alternately performed until a specified termination criteria is met. The selected basis vectors and optimized hyperparameters are stored in at least one tangible computer readable medium organized in a manner to be usable as the model of the sparse GP classifier. | 06-24-2010 |
20100161652 | RAPID ITERATIVE DEVELOPMENT OF CLASSIFIERS - A classifier development process seamlessly and intelligently integrates different forms of human feedback on instances and features into the data preparation, learning and evaluation stages. A query utility based active learning approach is applicable to different types of editorial feedback. A bi-clustering based technique may be used to further speed up the active learning process. | 06-24-2010 |
20100241639 | APPARATUS AND METHODS FOR CONCEPT-CENTRIC INFORMATION EXTRACTION - Disclosed are methods and apparatus for extracting (or annotating) structured information from web content. Web content of interest from a particular domain is represented as one or more tree instances having a plurality of branching nodes that each correspond to a web object such that the tree instances correspond to one or more structured data instances. The particular domain is associated with domain knowledge that includes one or more presentation rulesets that each specifies a particular structure for a set of data instances, a domain-specific concept labeler, one or more specified properties of the web objects in the tree instances, and a concept schema that specifies a representation of the data to be extracted from the web content. A structured data instance that conforms to the concept schema is extracted from the one or more tree instances based on the domain knowledge for the particular domain. Extraction of the structured data instances is accomplished by (i) using the domain-specific concept labeler to annotate a subset of nodes of the tree instances; and (ii) using a locally adaptive concept annotator to extract the structured data instances based on the annotated segments and the local properties associated with such annotated segments. The extracted structured data instance is stored as structured output records in a database. | 09-23-2010 |
20100274770 | TRANSDUCTIVE APPROACH TO CATEGORY-SPECIFIC RECORD ATTRIBUTE EXTRACTION - Disclosed are methods and apparatus for segmenting and labeling a collection of token sequences. A plurality of segments of one or more tokens in a token sequence collection are partially labeled with labels from a set of target labels using high precision domain-specific labelers so as to generate a partially labeled sequence collection having a plurality of labeled segments and a plurality of unlabeled segments. Any label conflicts in the partially labeled sequence collection are resolved. One or more of the labeled segments of the partially labeled sequence collection are expanded so as to cover one or more additional tokens of the partially labeled sequence collection. A statistical model, for labeling segments using local token and segment features of the sequence collection, is trained based on the partially labeled sequence collection. This trained model is then used to label the unlabeled segments and the labeled segments of the sequence collection so as to generate a labeled sequence collection. The labeled sequence collection is then stored as structured output records in a database. | 10-28-2010 |
20110016065 | EFFICIENT ALGORITHM FOR PAIRWISE PREFERENCE LEARNING - In one embodiment, training a ranking model comprises: accessing the ranking model and an objective function of the ranking model; accessing one or more preference pairs of objects, wherein for each of the preference pairs of objects comprising a first object and a second object, there is a preference between the first object and the second object with respect to the particular reference, and the first object and the second object each has a feature vector comprising one or more feature values; and training the ranking model by minimizing the objective function using the preference pairs of objects, wherein for each of the preference pairs of objects, a difference between the first feature vector of the first object and the second feature vector of the second object is not calculated. | 01-20-2011 |
20110087646 | Method and System for Form-Filling Crawl and Associating Rich Keywords - Techniques are provided for the efficient location, processing, and retrieval of local product information derived from web pages generally locatable through form queries submitted to web pages often referred to as the “deep” or “hidden” web. In an embodiment, information such as product information and dealer-location information is located on a web page form such as a dealer-locator form. After location of a suitable web page form, editorial wrapping is performed to create an automated information extraction process. Using the automated information extractor, deep-web crawling is performed. A grid-based extraction of individual business records is performed, and matching and ingestion are performed in conjunction with a business listing database. Finally, metadata tags are added to entries in the business listing database. Metadata tags also may be added to entries in other databases. | 04-14-2011 |
20110099131 | PAIRWISE RANKING-BASED CLASSIFIER - The present invention provides methods and systems for binary classification of items. Methods and systems are provided for constructing a machine learning-based and pairwise ranking method-based classification model for binary classification of items as positive or negative with regard to a single class, based on training using a training set of examples including positive examples and unlabelled examples. The model includes only one hyperparameter and only one threshold parameter, which are selected to optimize the model with regard to constraining positive items to be classified as positive while minimizing a number of unlabelled items classified as positive. | 04-28-2011 |
20110113063 | METHOD AND SYSTEM FOR BRAND NAME IDENTIFICATION - A method for identifying a brand name is described herein. The method involves obtaining category keywords associated with a category, designating a subgroup of the category keywords as brand name keywords for a particular brand name, receiving a search term, determining that the search term is a brand name keyword, and identifying the particular brand name corresponding to the brand name keyword. | 05-12-2011 |
20110302148 | System and Method for Indexing Food Providers and Use of the Index in Search Engines - Methods, systems and computer readable mediums are provided for indexing network resources. One method includes accessing, using one or more computer systems, a data store of menu items. The method further includes accessing identification information associated with one or more food providers from one or more data sources. One or more network resources are crawled based on the identification information to search for one or more menu items in the data store of menu items associated with corresponding ones of the food providers. Using the one or more computing systems, an index feed is generated, the index feed comprising the identification information of one or more of the food providers, and one or more menu items associated with the identification information of corresponding food providers based on the crawl and search. | 12-08-2011 |
20120109637 | EXTRACTING RICH TEMPORAL CONTEXT FOR BUSINESS ENTITIES AND EVENTS - Methods and apparatus for performing computer-implemented extraction of temporal information for business entities and events are disclosed. In one embodiment, a sequence of text is obtained. A label is assigned to one or more of a plurality of segments of the text such that each of the one or more of the plurality of segments of the text is classified as temporal data in one of a plurality of classes of temporal data. One or more rules are applied to the one or more segments of the text that have been classified as temporal data to generate a structured representation of the temporal data, where the rules include one or more schematic rules. Each of the schematic rules pertains to one or more of the plurality of classes of temporal data and indicates a structure in which temporal data in the corresponding one or more of the plurality of classes is to be stored. | 05-03-2012 |