Abstract: A Learning Enhanced Simulated Annealing (LESA) method is provided. Based on a Simulated Annealing (SA) framework, this method adds a Knowledge Base (KB) initialized at the beginning of the search and updated at each iteration, which memorizes a portion of the search history and guides the further search through a KB trial generator. The basic idea of LESA is that its search history is stored in a KB, and a KB trial generator extracts information from it and uses it to generate a new trial. The next move of the search is the weighted sum of the trial generated by the KB trial generator and the trial generated by the usual SA trial generator. The knowledge base is then updated after each search iteration.

Abstract: A system that can automatically narrow the search space or recognition scope within an activity-centric environment based upon a current activity or set of activities is provided. In addition, the activity and context data can also be used to rank the results of the recognition or search activity. In accordance with the domain scoping, natural language processing (NLP) as well as other types of conversion and recognition systems can dynamically adjust to the scope of the activity or group of activities thereby increasing the recognition systems accuracy and usefulness. In operation, a user context, activity context, environment context and/or device profile can be employed to effectuate the scoping. As well, the system can combine context with extrinsic data, including but not limited to, calendar, profile, historical activity data, etc. in order to define the parameters for an appropriate scoping.

Abstract: A crystal lookup table used to define a matching relationship between a signal position of a detected event in a PET scanner and a corresponding detector pixel location is generated using a neural network-based algorithm, and is implemented by a FPGA.

Abstract: A two-phase process quickly and accurately identifies representations of the same items within a collection of item representations. In the first phase, referred to as a “blocking phase,” frequency information indicating the frequency with which terms appear within the collection of item representations is used to quickly identify “candidate pairs” (i.e., pairs of item representations that have a relatively high probability of matching). The blocking phase results in a reduced subset of the data for further analysis during the second phase. In the second phase, referred to as a “matching phase,” the candidate pairs are analyzed using fuzzy matching functions to accurately identify “matching pairs” (i.e., representations of the same items).

Abstract: A method and apparatus are disclosed for selecting interaction policies. Values may be provided for a group of parameters for user models. Interaction policies within a specific tolerance of an optimal interaction policy for the user models may be learned. Up to a predetermined number of the learned interaction policies, within a specific tolerance of an optimal policy for the user models, may be selected for a wireless communication device. The wireless communication device, including the selected interaction policies, may determine whether any of a group of parameters, representing a user preference or contextual information with respect to use of the wireless communication device, is updated. When any of the group of parameters has been updated, the wireless communication device may select one of the selected interaction policies, such that the selected one of the selected interaction policies may determine a better interaction behavior for the wireless communication device.

Abstract: In one embodiment, a method comprises creating at least one individualized language resource, creating at least one individualized language rule referencing at least one of said individualized language resource, and transforming said at least one individualized language rule into computer executable format.

Abstract: A system and method is disclosed which integrates a machine learning solution into a large scale, distributed transaction processing system using a supporting architecture comprising a combination of computer hardware and software. Methods of using a system comprising such supporting architecture provide application designers access to the functionality included in a machine learning solution, but might also provide additional functionality not supported by the machine learning solution itself.

Abstract: Systems and methods for matching objects against a set of rules are described. The present invention is a novel rule execution algorithm that generally operates with greater efficiency than known algorithms. The algorithm uses a test analyzer to determine the relationships that exist between pairs of tests within a ruleset. Each rule is then translated into loops and tests, and merged into a unified series of loops and tests using the output of the test analyzer. The algorithm then generates pattern matching code corresponding to the unified series of loops and tests for evaluation by a virtual machine, and auxiliary code that provides object manipulations and rule actions at the service of the pattern matching code. In its runtime, the algorithm loads objects into the pattern matching code through an access interface. The pattern matching code is then executed by the virtual machine against the loaded objects.

Abstract: In a pre-processing step prior to training a learning machine, pre-processing includes reducing the quantity of features to be processed using feature selection methods selected from the group consisting of recursive feature elimination (RFE), minimizing the number of non-zero parameters of the system (l0-norm minimization), evaluation of cost function to identify a subset of features that are compatible with constraints imposed by the learning set, unbalanced correlation score, transductive feature selection and single feature using margin-based ranking. The features remaining after feature selection are then used to train a learning machine for purposes of pattern classification, regression, clustering and/or novelty detection.

Abstract: The invention is directed to a method for training at least one learning-capable system comprising the steps of providing a predetermined training data set corresponding to a predetermined number of subjects comprising a predetermined input data set and a predetermined outcome data set, augmenting the input data set and/or the outcome data set, and training each learning-capable system using the augmented input data set and/or the augmented outcome data set.

Abstract: A method, system and software arrangement in accordance with an exemplary embodiment of the present invention are provided to extract descriptive narrative from numerical experimental data augmented with ontological controlled vocabulary. One exemplary application of such system, method and software arrangement is in organizing gene-expression time course data in terms of biological processes that may be activated and deactivated as the biological system responds to its normal or perturbed environment. The present invention may also have biological applications to drug-or-vaccine discovery, understanding behavior of a cell in an altered diseased state (e.g., cancer, neuro-degeneration or auto-immune disease, etc.), genetically modifying a natural wild-type organism, genetic-engineering, etc. Other exemplary applications may include understanding neural behavior, market behavior of a population of users interacting on the Internet, etc.

Abstract: Disclosed is a controller that includes a curve matching mechanism. The curve matching mechanism employs Fuzzy Logic to compare input curves to standard curves and to thereby characterize the input curves. Also disclosed in an automotive environment for the curve matching mechanism in which a Fuzzy Controller is used to receive time series data (i.e., input curves) and to present the input curves to the curve matching mechanism. The controller then uses the output of the curve matching mechanism to adjust engine function.

Abstract: An autonomous classification device which enables the creation of autonomous classifiers that are easy to deploy, adapt and optimize in the environment in which they are used. The classifier is autonomous in that it can perform three functions that define autonomic systems: automatically configure itself in an environment, optimize its performance using the environment and mechanisms for performance, and continually adapting to improve performance and heal itself in a changing environment.

Abstract: A method of selecting and presenting content based on learned user preferences is provided. The method includes receiving search input from the user for identifying desired content items and receiving content selection actions from the user. The method further includes analyzing the date, day, and time of content selection actions by the user and analyzing descriptive terms associated with the selected content items to learn a periodicity of user selections of similar content items. In response to subsequent searches by the user, the method calls for selecting and ordering a collection of content items for presentation to the user based on comparing the user's search input to descriptive terms associated with content items and based on the learned periodicities of the user.

Abstract: An expert system having a data storage device and a processor. The data storage device stores models having attributes, objectives having rules for evaluating the models, and strategies having rules for modifying the attributes. The processor evaluates a selected model in accordance with a selected objective and having the variable attribute set in accordance with a selected strategy to determine a characteristic value associated with the selected model and the variable attribute setting. The processor also stores information associated with improved results in the data storage device.

Abstract: Events may be identified by storing information in response to activating an event stamp function. As a result of activating the event function, the information collected may immediately be compared to event information in a database. Alternatively, the information collected may later be compared to event information in a database. One or more candidates for the event of interest may be automatically or manually retrieved, and the user may decide whether a candidate event of the one or more candidates correspond to the event of interest. Alternatively, a purchase of an item related to the event may be automatically made in response to activating the event stamp.

Abstract: A method and system for controlling message flow in distributed stream processing. State transition probabilities in a Markov model having one state per staleness value of data are determined for sending or withholding updates of data to subscribers using expected message rates from an information provider. A cost function annotates each state transition in the model with a state transition cost for each decision to “send” or “withhold”. A propagation policy specifying whether to send or withhold the message is determined for each state. The propagation policy is then deployed. If a new message comprising an update of data is received during a lapsed time unit, a staleness value of the data held by subscribers is increased. The propagation policy is used to determine whether to send or withhold the message. If the message should be sent, the message is propagated and the staleness value of the data is reset.

Abstract: A system and method for classifying text includes a pre-processor, a knowledge base, and a statistical engine. The pre-processor identifies concepts in the text and creates a structured text object that contains the concepts. The structured text object is then passed to a statistical engine, which applies statistical information provided in nodes of a knowledge base to the structured text object in order to calculate a set of match scores, each match score representing the relevance of the text to an associated one of a plurality of predefined categories. The pre-processor may be implemented in the form of an interpreter which selects and executes a script that includes language- and scenario-specific instructions for performing linguistic and semantic analysis of the text.

Abstract: A method and computer program product to capture expert knowledge and data using probabilistic models. A custom layered structure and nodes reduce the complexity of the model, allowing for representation of the model using tables. An editor is used for entry and verification of expert knowledge and data into tables and a probabilistic model is generated from the tables.

Abstract: Information presented to a user via an information access system is ranked according to a prediction of the likely degree of relevance to the user's interests. A profile of interests is stored for each user having access to the system. Items of information to be presented to a user are ranked according to their likely degree of relevance to that user and displayed in order of ranking. The prediction of relevance is carried out by combining data pertaining to the content of each item of information with other data regarding correlations of interests between users. A value indicative of the content of a document can be added to another value which defines user correlation, to produce a ranking score for a document. Alternatively, multiple regression analysis or evolutionary programming can be carried out with respect to various factors pertaining to document content and user correlation, to generate a prediction of relevance.