| Patent application number | Description | Published |
|---|
| 20090018995 | Semi-supervised visual clustering - A clustering system includes a visual mapping sub-system configured to display an N-dimensional to two- or three-dimensional mapping of items to be clustered, where N is greater than three, the mapping having mapping parameters for the N-dimensions. A user interface sub-system is configured to receive user inputted values for the mapping parameters, user inputted values selecting whether selected mapping parameters are fixed or adjustable, and user inputted values associating selected items with selected groups. An adjustment sub-system is configured to adjust the adjustable mapping parameters, without adjusting any fixed mapping parameters, to improve a measure of distinctness of one or more groups of items in the two- or three-dimensional mapping. | 01-15-2009 |
| 20090157572 | STACKED GENERALIZATION LEARNING FOR DOCUMENT ANNOTATION - A document annotation method includes modeling data elements of an input document and dependencies between the data elements as a dependency network. Static features of at least some of the data elements are defined, each expressing a relationship between a characteristic of the data element and its label. Dynamic features are defined which define links between an element and labels of the element and of a second element. Parameters of a collective probabilistic model for the document are learned, each expressing a conditional probability that a first data element should be labeled with information derived from a label of a neighbor data element linked to the first data element by a dynamic feature. The learning includes decomposing a globally trained model into a set of local learning models. The local learning models each employ static features to generate estimations of the neighbor element labels for at least one of the data elements. | 06-18-2009 |
| 20090271338 | SCALABLE FEATURE SELECTION FOR MULTI-CLASS PROBLEMS - In a feature filtering approach, a set of relevant features and a set of training objects classified respective to a set of classes are provided. A candidate feature and a second feature are selected from the set of relevant features. An approximate Markov blanket criterion is computed that is indicative of whether the candidate feature is redundant in view of the second feature. The approximate Markov blanket criterion includes at least one dependency on less than the entire set of classes. An optimized set of relevant features is defined, consisting of a sub-set of the set of relevant features from which features indicated as redundant by the selecting and computing are removed. | 10-29-2009 |
| 20100150448 | METHOD OF FEATURE EXTRACTION FROM NOISY DOCUMENTS - Aspect of the exemplary embodiment relate to a method and apparatus for automatically identifying features that are suitable for use by a classifier in assigning class labels to text sequences extracted from noisy documents. The exemplary method includes receiving a dataset of text sequences, automatically identifying a set of patterns in the text sequences, and filtering the patterns to generate a set of features. The filtering includes at least one of filtering out redundant patterns and filtering out irrelevant patterns. The method further includes outputting at least some of the features in the set of features, optionally after fusing features which are determined not to affect the classifiers accuracy if they are merged. | 06-17-2010 |
| 20100205181 | AVERAGE CASE ANALYSIS FOR EFFICIENT SPATIAL DATA STRUCTURES - A computer performed method models a spatial index having n spatial regions defined in a multidimensional space using a tree-based model representing an infinite number of arrangements of n spatial regions in the multidimensional space allowable by the spatial index using a finite number of tree representations, computes an average retrieval complexity measure for content retrieval using the spatial index based on the tree based model, and provides a spatial index recommendation based on the average retrieval complexity measure. In some embodiments a spatial index selection module selects the spatial index based on average retrieval complexity measures for candidate spatial indices that are functionally dependent upon a number of spatial regions to be defined by the spatial index. | 08-12-2010 |
| 20100306141 | Method for transforming data elements within a classification system based in part on input from a human annotator/expert - A method and system are provided for classifying data items such as a document based upon identification of element instances within the data item. A training set of classes is provided where each class is associated with one or more features indicative of accurate identification of an element instance within the data item. Upon the identification of the data item with the training set, a confidence factor is computed that the selected element instance is accurately identified. When a selected element instance has a low confidence factor, the associated features for the predicted class are changed by an annotator/expert so that the changed class definition of the new associated feature provides a higher confidence factor of accurate identification of element instances within the data item. | 12-02-2010 |
| 20110022599 | SCALABLE INDEXING FOR LAYOUT BASED DOCUMENT RETRIEVAL AND RANKING - A computer-based method and a system for indexing, querying, and ranking documents based on layout are provided. The method includes providing a plurality of documents to computer memory, extracting layout blocks from the provided documents, clustering the layout blocks into a plurality of layout block clusters, computing a representative block for each of the layout block clusters, generating a document index for each provided document based on the layout blocks of the document and the computed representatives blocks, clustering the created document indexes into a plurality of document index clusters, and generating a representative cluster index for each of the document index clusters. The indexes generated, together with the representative blocks and document index clusters, can be stored and used for retrieval of documents responsive to a layout query. | 01-27-2011 |
| 20110103682 | MULTI-MODALITY CLASSIFICATION FOR ONE-CLASS CLASSIFICATION IN SOCIAL NETWORKS - A classification apparatus, method, and computer program product for multi-modality classification are disclosed. For each of a plurality of modalities, the method includes extracting features from objects in a set of objects. The objects include electronic mail messages. A representation of each object for that modality is generated, based on its extracted features. At least one of the plurality of modalities is a social network modality in which social network features are extracted from a social network implicit in the set of electronic mail messages. A classifier system is trained based on class labels of a subset of the set of objects and on the representations generated for each of the modalities. With the trained classifier system, labels are predicted for unlabeled objects in the set of objects. | 05-05-2011 |
| 20110106732 | METHOD FOR CATEGORIZING LINKED DOCUMENTS BY CO-TRAINED LABEL EXPANSION - Systems and methods are described that facilitate categorizing a group of linked web pages. A plurality of web pages each contains at least one link to another page within the group. A feature analyzer evaluates features associated with the one or more web pages to identify content, layout, links and/or metadata associated with the one or more web pages and identifies features that are labeled and features that are unlabeled. A graphing component creates a vector associated with each web page feature wherein vectors for unlabeled features are determined by their graphical proximity to features that are labeled. A co-training component receives the graph of vectors from the graphing component and leverages the disparate web page features to categorize each aspect of each feature of the page. A page categorizer receives aspect categorization information from the co-training component and categorizes the web page based at least upon this information. | 05-05-2011 |
