Abstract: Techniques for a classification system with methodology for enhanced verification are described. In one approach, a classification computer trains a classifier based on a set of training documents. After training is complete, the classification computer iterates over a collection unlabeled documents uses the trained classifier to predict a label for each unlabeled document. A verification computer retrieves one of the documents assigned a label by the classification computer. The verification computer then generates a user interface that displays select information from the document and provides an option to verify the label predicted by the classification computer or provide an alternative label. The document and the verified label are then fed back into the set of training documents and are used to retrain the classifier to improve subsequent classifications. In addition, the document is indexed by a query computer based on the verified label and made available for search and display.

Abstract: Methods and systems to reduce the number of factors or variables that need to be considered in generating a function such as a prediction function. The methods and systems may involve receiving a first set of factors and a data set associated with the first set of factors, calculating an importance value of each factor in the first set of factors, and selecting, based on the importance values, a second set of factors. In some embodiments, the methods and systems also include generating the prediction function based on the data set and the second set of factors.

Abstract: Dynamically updating neural network systems may be implemented to generate, train, evaluate and update artificial neural network data structures used by content distribution networks. Such systems and methods described herein may include generating and training neural networks, using neural networks to perform predictive analysis and other decision-making processes within content distribution networks, evaluating the performance of neural networks, and generating and training pluralities of replacement candidate neural networks within cloud computing architectures and/or other computing environments.

Abstract: Methods and apparatus are provided for automated object classification using temperature profiles. An object in an environment (such as an exemplary data center) is classified by obtaining a surface temperature profile of the object; and classifying the object as a particular type of equipment based on the obtained surface temperature profile. The surface temperature profile of the object can be compared to a plurality of predefined characteristic surface temperature profiles each associated with a given type of equipment.

Abstract: Methods and apparatus are provided for automated object classification using temperature profiles. An object in an environment (such as an exemplary data center) is classified by obtaining a surface temperature profile of the object; and classifying the object as a particular type of equipment based on the obtained surface temperature profile. The surface temperature profile of the object can be compared to a plurality of predefined characteristic surface temperature profiles each associated with a given type of equipment.

Abstract: A machine, system and method for user-guided teaching of deictic references and referent objects of deictic references to a conversational system. The machine includes a system bus for communicating data and control signals received from the conversational system to the computer system, a data and control bus for connecting devices and sensors in the machine, a bridge module for connecting the data and control bus to the system bus, respective machine subsystems coupled to the data and control bus, the respective machine subsystems having a respective user interface for receiving a deictic reference from a user, a memory coupled to the system bus for storing deictic references and objects of the deictic references learned by the conversational system and a central processing unit coupled to the system bus for executing the deictic references with respect to the objects of the deictic references learned.

Abstract: A system, method and computer program product for automatic document classification, including an extraction module configured to extract structural, syntactical and/or semantic information from a document and normalize the extracted information; a machine learning module configured to generate a model representation for automatic document classification based on feature vectors built from the normalized and extracted semantic information for supervised and/or unsupervised clustering or machine learning; and a classification module configured to select a non-classified document from a document collection, and via the extraction module extract normalized structural, syntactical and/or semantic information from the selected document, and generate via the machine learning module a model representation of the selected document based on feature vectors, and match the model representation of the selected document against the machine learning model representation to generate a document category, and/or classificatio

Abstract: There is provided an information processing apparatus for presenting knowledge information that is similar to inputted knowledge information, by using a knowledge system including relation information between a plurality of knowledge information items, the apparatus comprising: an input unit configured to receive first knowledge information, and second knowledge information that is associated with the first knowledge information; a relation acquisition unit configured to acquire first relation information indicating a relation that the first knowledge information has with respect to the second knowledge information, from the knowledge system; a knowledge acquisition unit configured to acquire knowledge information that has the relation indicated by the first relation information with respect to the second knowledge information, from the knowledge system; and an output unit configured to output the knowledge information acquired by the knowledge acquisition unit as knowledge information similar to the first kn

Abstract: Apparatus and methods for salient feature detection by a spiking neuron network. The network may comprise feature-specific units capable of responding to different objects (red and green color). The plasticity mechanism of the network may be configured based on difference between two similarity measures related to activity of different unit types obtained during network training. One similarity measure may be based on activity of units of the same type (red). Another similarity measure may be based on activity of units of one type (red) and another type (green). Similarity measures may comprise a cross-correlogram and/or mutual information determined over an activity window. During network operation, the activity based plasticity mechanism may be used to potentiate connections between units of the same type (red-red). The plasticity mechanism may be used to depress connections between units of different types (red-green). The plasticity mechanism may effectuate detection of salient features in the input.

Abstract: Apparatus and methods for universal node design implementing a universal learning rule in a mixed signal spiking neural network. In one implementation, at one instance, the node apparatus, operable according to the parameterized universal learning model, receives a mixture of analog and spiking inputs, and generates a spiking output based on the model parameter for that node that is selected by the parameterized model for that specific mix of inputs. At another instance, the same node receives a different mix of inputs, that also may comprise only analog or only spiking inputs and generates an analog output based on a different value of the node parameter that is selected by the model for the second mix of inputs. In another implementation, the node apparatus may change its output from analog to spiking responsive to a training input for the same inputs.

Abstract: The invention provides, in some aspects, a computer-implemented method for enabling enhanced functionality in a software application. The method includes executing, on a computer, an enhancement engine that is communicatively coupled to a rules base (or other store that contains rules) and/or a rules engine that executes rules (e.g., from the rules base). The enhancement engine receives a request to enable enhanced functionality in an application that is defined, at least in part, by a plurality of such rules, where the request specifies a selected rule in the application for such enhancement. The enhancement engine identifies (or ascertains) a new rule at least partially providing the enhanced functionality and (i) updates the rules base (or other store) to include the new rule along with the others that define at least a portion of the application and/or (ii) effects execution by the rules engine of the new rule along with those others.

Abstract: An extended collaboration event monitoring system monitors users' interactions with computer software applications and detects and extracts events. The system intelligently determines whether the extracted events trigger undetected events or other action items. The system provides the extracted events to a social networking client that translates the extracted events and returns the translated data to the system. The system publishes the translated data to a social networking/collaboration interface embedded into the interface of the computer software application being utilized by one of the users. The system not only publishes the translated data corresponding to a user's interactions with the computer software application to that user's interface, but also publishes the user's interactions with the computer software application to interfaces corresponding to other project team members as well.

Abstract: System and method related to a distributed collaborative knowledge generation system are disclosed. The distributed collaborative knowledge generation system includes one or more databases configured to store content information related to the distributed collaborative knowledge generation system, a search services module configured to search for the content information, a web services module configured to retrieve and gather the content information, a presentation services module configured to share the content data with multiple users, and a data services module configured to manage the content information by providing interfaces between the one or more databases, the search services module, the web services module, and the presentation services module.

Abstract: A control apparatus and methods using context-dependent difference learning for controlling e.g., a plant. In one embodiment, the apparatus includes an actor module and a critic module. The actor module provides a control signal for the plant. The actor module is subject to adaptation, which is performed to optimize control strategy of the actor. The adaptation is based upon the reinforcement signal provided by the critic module. The reinforcement signal is calculated based on the comparison of a present control performance signal observed for a certain context signal, with a control performance signal observed for the same context in the past.

Abstract: In certain embodiments, a method includes formulating an optimization problem to determine a plurality of model parameters of a system to be modeled. The method also includes solving the optimization problem to define an empirical model of the system. The method further includes training the empirical model using training data. The empirical model is constrained via general constraints relating to first-principles information and process knowledge of the system.

Abstract: A method and apparatus for deriving diagnostic data about a technical system utilizing learning metrics gained by at least one data driven learning process while generating and updating soft sensor models of said technical system.

Abstract: According to one embodiment, a system is provided. The system includes a memory, at least one processor coupled to the memory and an object network modeler component executable by the at least one processor. The memory stores an object network including a plurality of objects, the plurality of objects including a first object, a second object, a third object, and a fourth object. The object network modeler component is configured to implicitly associate, within the object network, the first object with the second object and explicitly associate, within the object network, the third object with the fourth object.

Abstract: The automatic selection and usage of a parser is disclosed. Raw data is received from a first remote device. At least a portion of the raw data is evaluated using a plurality of rules. A confidence measure is determined for at least some of the rules. An indication that the raw data pertains to a source is provided as output when the confidence measure exceeds a threshold.

Abstract: A method and system of classifying documents is provided. The method includes receiving a stream of documents from at least one user wherein each document includes a topic of information relating to a customer support issue or sentiment. The method includes classifying each of the received documents using a plurality of trained classifiers, the classification based on a voting by the trained classifiers, each document labeled according to a similar topic. A drift of the topic of one or more of the classifications is determined wherein the drift is related to the received documents that include information relating to an unclassified customer support issue or sentiment. If the determined drift exceeds a predetermined threshold range, rebuilding the plurality of classifiers to include a second set of classifiers trained to recognize the unclassified customer support issue or sentiment.

Abstract: A classifier that disambiguates among entities based on a dictionary, such as corpus of documents about those entities, is built by incorporating probabilities that an entity exists that is not in the dictionary. Given a document it is associated by the classifier with an entity. By incorporating out of collection probabilities into the classifier, a higher level of confidence in the match between an entity and a document is achieved.