4564results about "Kernel methods" patented technology

Systems and methods for massively parallel processing on an accelerator that includes a plurality of processing cores. Each processing core includes multiple processing chains configured to perform parallel computations, each of which includes a plurality of interconnected processing elements. The cores further include multiple of smart memory blocks configured to store and process data, each memory block accepting the output of one of the plurality of processing chains. The cores communicate with at least one off-chip memory bank.

A separation surface set storage part stores information defining a plurality of separation surfaces which separate a feature space into at least one known class region respectively corresponding to at least one known class and an unknown class region. Each of the at least one known class region is separated from outside region by more than one of the plurality of separation surfaces which do not intersect to each other. A data classification apparatus determine a classification of a classification target data whose inner product in the feature space is calculable by calculating to which region of the at least one known class region and the unknown class region determined by the information stored in the separation surface set storage part the classification target data belongs. A method and apparatus for data classification which can simultaneously perform identification and outlying value classification with high reliability in a same procedure are provided.

The present invention is directed to a computer automated method of selectively identifying a user specified behavior of a crowd. The method comprises receiving video data but can also include audio data and sensor data. The video data contains images a crowd. The video data is processed to extract hierarchical human and crowd features. The detected crowd features are processed to detect a selectable crowd behavior. The selected crowd behavior detected is specified by a configurable behavior rule. Human detection is provided by a hybrid human detector algorithm which can include Adaboost or convolutional neural network. Crowd features are detected using textual analysis techniques. The configurable crowd behavior for detection can be defined by crowd behavioral language.

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 (l_o-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 and transductive feature selection. 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.

A 1D-CNN-based ((one-dimensional convolutional neural network)-based) distributed optical fiber sensing signal feature learning and classification method is provided, which solves a problem that an existing distributed optical fiber sensing system has poor adaptive ability to a complex and changing environment and consumes time and effort due to adoption of manually extracted distinguishable event features, The method includes steps of: segmenting time sequences of distributed optical fiber sensing acoustic and vibration signals acquired at all spatial points, and building a typical event signal dataset; constructing a 1D-CNN model, conducting iterative update training of the network through typical event signals in a training dataset to obtain optimal network parameters, and learning and extracting 1D-CNN distinguishable features of different types of events through an optimal network to obtain typical event signal feature sets; and after training different types of classifiers through the typical event signal feature sets, screening out an optimal classifier.

Methods for training machines to categorize data, and/or recognize patterns in data, and machines and systems so trained. More specifically, variations of the invention relates to methods for training machines that include providing one or more training data samples encompassing one or more data classes, identifying patterns in the one or more training data samples, providing one or more data samples representing one or more unknown classes of data, identifying patterns in the one or more of the data samples of unknown class(es), and predicting one or more classes to which the data samples of unknown class(es) belong by comparing patterns identified in said one or more data samples of unknown class with patterns identified in said one or more training data samples. Also provided are tools, systems, and devices, such as support vector machines (SVMs) and other methods and features, software implementing the methods and features, and computers or other processing devices incorporating and/or running the software, where the methods and features, software, and processors utilize specialized methods to analyze data.

Techniques for creating training sets for predictive modeling are provided. In one aspect, a method for generating training data from an unlabeled data set is provided which includes the following steps. A small initial set of data is selected from the unlabeled data set. Labels are acquired for the initial set of data selected from the unlabeled data set resulting in labeled data. The data in the unlabeled data set is clustered using a semi-supervised clustering process along with the labeled data to produce data clusters. Data samples are chosen from each of the clusters to use as the training data. The selecting, presenting, clustering and choosing steps are repeated with one or more additional sets of data selected from the unlabeled data set until a desired amount of training data has been obtained, wherein at each iteration an amount of the labeled data is increased.

A classification system includes memory which stores, for each of a set of classes, a classifier model for assigning a class probability to a test sample from a target domain. The classifier model has been learned with training samples from the target domain and from at least one source domain. Each classifier model models the respective class as a mixture of components, the component mixture including a component for each source domain and a component for the target domain. Each component is a function of a distance between the test sample and a domain-specific class representation which is derived from the training samples of the respective domain that are labeled with the class, each of the components in the mixture being weighted by a respective mixture weight. Instructions, implemented by a processor, are provided for labeling the test sample based on the class probabilities assigned by the classifier models.

Providing enhanced video content includes processing at least one video feed through at least one spatiotemporal pattern recognition algorithm that uses machine learning to develop an understanding of a plurality of events and to determine at least one event type for each of the plurality of events. The event type includes an entry in a relationship library detailing a relationship between two visible features. Extracting and indexing a plurality of video cuts from the video feed is performed based on the at least one event type determined by the understanding that corresponds to an event in the plurality of events detectable in the video cuts. Lastly, automatically and under computer control, an enhanced video content data structure is generated using the extracted plurality of video cuts based on the indexing of the extracted plurality of video cuts.

The Collateral Management with Blockchain and Smart Contracts Apparatuses, Methods and Systems (“CMBSC”) transforms borrow transaction request inputs via CMBSC components into borrow transaction init notification, borrow transaction sync notification outputs. A borrow transaction request associated with a borrow transaction is obtained. Transaction attributes associated with the borrow transaction are stored in a database. The transaction process optimizer component is notified regarding the borrow transaction. A blockchain sync notification associated with the borrow transaction is obtained from the transaction process optimizer component. The stored transaction attributes associated with the borrow transaction are filtered. A smart contract associated with the borrow transaction is generated. The generated smart contract is sent to a blockchain node of a blockchain network. A smart contract notification associated with the smart contract is received. A push notification regarding the smart contract notification is provided to a user interface component of a user's client.

Systems and techniques are provided for linking subjects in an area of real space with user accounts. The user accounts are linked with client applications executable on mobile computing devices. A plurality of cameras are disposed above the area. The cameras in the plurality of cameras produce respective sequences of images in corresponding fields of view in the real space. A processing system is coupled to the plurality of cameras. The processing system includes logic to determine locations of subjects represented in the images. The processing system further includes logic to match the identified subjects with user accounts by identifying locations of the mobile computing devices executing client applications in the area of real space and matching locations of the mobile computing devices with locations of the subjects.

A machine learning system for ranking a collection of filtered propensity to failure metrics of like components within an electrical grid that includes a raw data assembly to provide raw data representative of the like components within the electrical grid; (b) a data processor, operatively coupled to the raw data assembly, to convert the raw data to more uniform data via one or more data processing techniques; (c) a database, operatively coupled to the data processor, to store the more uniform data; (d) a machine learning engine, operatively coupled to the database, to provide a collection of propensity to failure metrics for the like components; (e) an evaluation engine, operatively coupled to the machine learning engine, to detect and remove non-complying metrics from the collection of propensity to failure metrics and to provide the collection of filtered propensity to failure metrics; and (f) a decision support application, operatively coupled to the evaluation engine, configured to display a ranking of the collection of filtered propensity to failure metrics of like components within the electrical grid.

The present invention relates to a method for closed-loop controlling a processing operation of a workpiece, comprising the steps of: (a) recording a pixel image at an initial time point of an interaction zone by means of a camera, wherein the workpiece is processed using an actuator having an initial actuator value; (b) converting the pixel image into a pixel vector; (c) representing the pixel vector by a sum of predetermined pixel mappings each multiplied by a corresponding feature value; (d) classifying the set of feature values on the basis of learned feature values into at least two classes of a group of classes comprising a first class of a too high actuator value, a second class of a sufficient actuator value and a third class of a too low actuator value at the initial time point; (e) performing a control step for adapting the actuator value by minimizing the error e_t between a quality indicator y_e and a desired value; and (f) repeating the steps (a) to (e) for further time points to perform a closed-loop controlled processing operation.

A traffic classifier has a plurality of binary classifiers, each associated with one of a plurality of calibrators. Each calibrator trained to translate an output score of the associated binary classifier into an estimated class probability value using a fitted logistic curve, each estimated class probability value indicating a probability that the packet flow on which the output score is based belongs to the traffic class associated with the binary classifier associated with the calibrator. The classifier training system configured to generate a training data based on network information gained using flow and packet sampling methods. In some embodiments, the classifier training system configured to generate reduced training data sets, one for each traffic class, reducing the training data related to traffic not associated with the traffic class.

Systems and methods implemented by a computer to detect abnormal behavior in a network include obtaining Performance Monitoring (PM) data including one or more of production PM data, lab PM data, and simulated PM data; determining a model based on machine learning training with the PM data; receiving live PM data from the network; utilizing the live PM data with the model to detect an anomaly in the network; and causing an action to address the anomaly.

A system is disclosed for chatbot conversation construction, and includes user interface, coupled to a conversation construction and management platform, configured to provide entry of intents, associated responses and search terms, and to construct and store conversation controllers, each based at least in part on one or more of the intents, and build conversation flows, each based at least in part on one or more of the conversation controllers. An artificial intelligence (AI) connector platform couples to the conversation construction and management platform, and is configured to receive natural language (NL) search data from a chatbot channel, format the NL search data into an input protocol of a selectable NL processing engine and receive a response from the NLP engine and, based at least in part on one or more of the conversation flows, convert the NLP engine response to a personalized response, and send the personalized response to the chatbot channel.

The present invention is directed to a method for detecting unknown malicious code, such as a virus, a worm, a Trojan Horse or any combination thereof. Accordingly, a Data Set is created, which is a collection of files that includes a first subset with malicious code and a second subset with benign code files and malicious and benign files are identified by an antivirus program. All files are parsed using n-gram moving windows of several lengths and the TF representation is computed for each n-gram in each file. An initial set of top features (e.g., up to 5500) of all n-grams IS selected, based on the DF measure and the number of the top features is reduced to comply with the computation resources required for classifier training, by using features selection methods. The optimal number of features is then determined based on the evaluation of the detection accuracy of several sets of reduced top features and different data sets with different distributions of benign and malicious files are prepared, based on the optimal number, which will be used as training and test sets. For each classifier, the detection accuracy is iteratively evaluated for all combinations of training and test sets distributions, while in each iteration, training a classifier using a specific distribution and testing the trained classifier on all distributions. The optimal distribution that results with the highest detection accuracy is selected for that classifier.