Patent application number | Description | Published |
20080208788 | METHOD AND SYSTEM FOR PREDICTING CUSTOMER WALLETS - A method (and system) of predicting an unobserved target variable includes building a graphical predictive model from domain knowledge, which takes advantage of conditional independence to facilitate inference about the unobserved target variable, given observations of other variables in the graphical predictive model from a plurality of information sources. | 08-28-2008 |
20080270088 | METHOD AND SYSTEM FOR CAUSAL MODELING AND OUTLIER DETECTION - A method (and system) for causal modeling includes modeling a data set using a reverse Bayesian forest. | 10-30-2008 |
20090055139 | PREDICTIVE DISCRETE LATENT FACTOR MODELS FOR LARGE SCALE DYADIC DATA - A method for predicting future responses from large sets of dyadic data includes measuring a dyadic response variable associated with a dyad from two different sets of data; measuring a vector of covariates that captures the characteristics of the dyad; determining one or more latent, unmeasured characteristics that are not determined by the vector of covariates and which induce local structures in a dyadic space defined by the two different sets of data; and modeling a predictive response of the measurements as a function of both the vector of covariates and the one or more latent characteristics, wherein modeling includes employing a combination of regression and matrix co-clustering techniques, and wherein the one or more latent characteristics provide a smoothing effect to the function that produces a more accurate and interpretable predictive model of the dyadic space that predicts future dyadic interaction based on the two different sets of data. | 02-26-2009 |
20090157589 | SYSTEM FOR OPINION RECONCILIATION - A system is disclosed for reconciling opinions generated by agents with respect to one or more predicates. The disclosed system may use observed variables and a probabilistic model including latent parameters to estimate a truth score associated with each of the predicates. The truth score, as well as one or more of the latent parameters of the probabilistic model, may be estimated based on the observed variables. The truth score generated by the disclosed system may enable publishers to reliably represent the truth of a predicate to interested users. | 06-18-2009 |
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 |
20100169158 | SQUASHED MATRIX FACTORIZATION FOR MODELING INCOMPLETE DYADIC DATA - A method of predicting a response relationship between elements of two sets includes: specifying a dyadic response matrix; specifying covariates that measure additional dyadic relationships; specifying a number of row clusters and a number of column clusters for clustering the rows and columns of the response matrix; specifying a rank for cluster factors that model average interactions between row clusters and column clusters by products of cluster factors; and determining prediction parameters for predicting responses between elements of the first set and the second set by improving a likelihood value that relates the prediction parameters to the response matrix, the covariates, the observation weights, the row clusters and the column clusters. Determining the prediction parameters includes: updating the prediction parameters for fixed assignments of row clusters and column clusters, and updating assignments for row clusters and column clusters for fixed prediction parameters. | 07-01-2010 |
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 |