Abstract: Systems and methods are disclosed that are configured to access a database that includes a list of members of a first group, a list of members of a second group, and ratings for at least some of the members of the second group. The ratings are attributed to the members of the first group. A machine learning training set is built for a particular member of the first group. The training set includes class labels corresponding to the particular member's ratings for the members of the second group, and features that include supplied and predicted ratings from at least a subset of processed members of the first group. A predictor for the particular member of the first group is trained based on the machine learning training set. The predictor corresponding to the particular member is used to generate predicted ratings for one or more members of the second group the particular member has not rated.

Abstract: Provided are, among other things, systems, methods and techniques for scanning information across multiple different devices. In one representative system, remote data-processing devices are provided with scanning applications that repeatedly scan information on their respective data-processing devices to identify matching data units that satisfy a specified matching criterion, the specified matching criterion including required matches against a set of screening digests, and then transmit characteristic information regarding the matching data units; and a central processing facility receives the characteristic information from the remote data-processing devices and determines whether the corresponding matching data units satisfy a policy criterion.

Abstract: Provided are, among other things, systems, methods and techniques for document-based processing. In one implementation, a document is input; features are extracted from it; an index is queried using at least a subset of the extracted features and, in response, identifications for selected document classifiers are received from a larger pool of document classifiers; the document is processed using individual ones of the selected document classifiers, thereby generating corresponding classifier outputs; and then, based on such classifier outputs, (1) the document is categorized within a computer database and/or (2) feedback information is provided to a user.

Abstract: In a method for assessing a plurality of electronic devices, cooling efficiencies for the plurality of electronic devices are calculated, where the cooling efficiencies comprise measures of energy usage requirements to respectively maintain the plurality of electronic devices within predetermined temperature ranges. In addition, the plurality of electronic devices are ranked according to their cooling efficiencies and the plurality of electronic devices are stored according to their rankings.

Abstract: Provided are systems, methods and techniques for machine learning. In one representative embodiment, a training set that includes training samples and corresponding assigned classification labels is obtained, and an automated classifier is trained against the training set. At least one of the training samples is selected and confirmation/re-labeling of it is requested. In response, a reply classification label is received and is used to retrain the automated classifier.

Abstract: Disclosed herein is an improved, metallized polymer film and method for preparing the same. Such films comprise a) at least one treated polymeric skin layer comprising butylene, and b) a metal layer deposited onto at least one surface of this polymeric skin layer. The polymeric skin layer can comprise a propylene-butylene copolymer or an ethylene-propylene-butylene terpolymer, with this copolymer or terpolymer containing from about 4 wt % to about 12 wt % of butylene. The polymeric skin layer is preferably preconditioned by subjecting an outermost surface thereof to plasma treatment which facilitates the deposition thereon of a strongly adhering metal layer which is preferably aluminum. The resulting multi-layer films exhibit metal adhesion bond strength of at least about 100 grams/25 mm. These films will preferably also exhibit a Water Vapor Transmission Rate of from about 0.05 to about 0.2 g/m2/24 hour, and an Oxygen Transmission Rate of from about 5.0 to about 20 cm3/m2/24 hour.

Abstract: Heat-sealable, multi-layer composite packaging structures that are suitable for packaging and having improved sealing properties and simplified construction as compared to prior art composite film constructions are disclosed. The inventive film structure includes a first substrate, such as paper, bonded such as by extrusion lamination, to a sealable, high-barrier film. This inventive “paper-adhesive-polymer” lamination may replace prior art “paper-adhesive-foil-adhesive” foil-based tandem laminations. An improved or comparable polymer film substrate may replace the “foil-adhesive” layer components of the popular incumbent prior art structure.

Abstract: According to an embodiment, the present invention pertains to a system for detecting airflow in a room. The system includes an airflow indicating device having a movable component whose movement substantially corresponds to airflow in a vicinity of the airflow indicating device. The system also includes cooling system components and a computer system configured to control the cooling system components substantially based upon movement of the movable component.

Abstract: A method, apparatus, and system are disclosed for selecting quality images from multiple captured images. One embodiment is an image capturing system. The system includes hardware for capturing a plurality of consecutive images and a processor for determining a quality indication for the consecutive images and for selecting one of the consecutive images based on the quality indication.

Abstract: Abstract of the Disclosure A method and system of providing training information for training a categorizer includes receiving a query relating to at least one category and identifying at least one case within a data set that matches the query. The method and system receives one of a first indication that the identified at least one case belongs to the category, and a second indication that the identified at least one case does not belong to the category. Training information is modified based on receiving one of the first indication and second indication.

Abstract: A first set of classifiers are trained to predict categories using attributes of cases. A second set of a classifiers are also trained to predict the categories using the attributes of the cases. In addition, the second set of classifiers use the predictions from at least one of the classifiers in the first set to make predictions.

Abstract: A classification count adjustment system for adjusting a count estimate of items in a dataset D classified into a class is disclosed. The system includes a count estimate produced by a classifier of the number of items in the dataset D classified into the class. The system further comprises one or more measures of behavior of the classifier, indicating the ability of the classifier to classify items into the class. The system further comprises a processor for computing an adjusted estimate based on the count estimate by the classifier and the one or more measures of behavior.

Abstract: An apparatus and methods for feature selection are disclosed. The feature selection apparatus and methods allow for determining a set of final features corresponding to features common to features within a set of frequent features of target dataset and a plurality of features within a training dataset. The feature selection apparatus and methods also allow for removing features within a target dataset and a training dataset that are not within a set of most predictive features.

Abstract: An apparatus and methods for feature selection and classifier builder are disclosed. The feature selection apparatus allows for removal of bias features. The classifier builder apparatus allows building a classifier using non-biased features. The feature selection methods disclosed teach how to remove bias features. The classifier builder methods disclosed teach how to build a classifier with non-biased features.

Abstract: A system and method for minimally predictive feature identification is disclosed. The method discloses: receiving an information collection including a set of features; generating a set of prediction values indicating a degree to which a first feature within the set of features predicts other features in the set; and identifying the first feature as a minimally predictive feature if each of the prediction values is within a predetermined range of threshold values. The system discloses means for practicing the method.

Abstract: A distributed data clustering system having an integrator and at least two computing units. Each computing unit is loaded with common global parameter values and a particular local data set. Each computing unit then generates local sufficient statistics based on the local data set and global parameter values. The integrator employs the local sufficient statistics of all the computing units to update the global parameter values.

Abstract: A query-centric system and process for distributing reverse indices for a distributed content system. Relevance ranking techniques in organizing distributed system indices. Query-centric configuration subprocesses (1) analyze query data, partitioning terms for reverse index server(s) (RIS), (2) distribute each partitioned data set by generally localizing search terms for the RIS that have some query-centric correlation, and (3) generate and maintain a map for the partitioned reverse index system terms by mapping the terms for the reverse index to a plurality of different index server nodes. Indexing subprocess element builds distributed reverse indices from content host indices. Routines of the query execution use the map derived in the configuration to more efficiently return more relevant search results to the searcher.

Abstract: A system and method for category discovery is disclosed. The method discloses: receiving an information collection including a set of strings; identifying positively predictive pairs of strings; identifying negatively predictive pairs of strings; joining positively predictive pairs of strings into a category; and splitting negatively predictive pairs of strings into different categories. The system discloses various elements, means and instructions for performing the method.

Abstract: The method discloses: scanning an original copy of a handwritten identifier; comparing the identifier to a set of prerecorded identifiers; adding the identifier to the set of prerecorded identifiers, if the identifier shares less than a predetermined similarity with any identifier within the set of prerecorded identifiers; linking the identifier to a set of information; scanning the original copy a second time; matching the second scan of the original copy with the first scan of the original copy; and retrieving the set of information in response to the matching. The system discloses: a computer system; a handwritten identifier; an imaging device for scanning an original copy of a handwritten identifier; an image processing and matching module for comparing the identifier to a set of prerecorded identifiers; a linking module for linking the identifier to a set of information.

Abstract: A method is provided for the identification of managed systems that are exceptional compared to comparable managed systems. The method involves selecting a set of managed systems wherein each managed system has a number of system configuration attributes and selecting a set of parameterizations relating to the managed systems. Patterns are determined for the parameterizations within the set of managed systems using a supervised machine learning algorithm. The managed systems are compared to the patterns and any managed systems that deviate from the patterns are isolated as exceptional managed systems. Isolated systems may be targeted for remedial action such as the allocation of support resources or system management resources.