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4897 results about "Learning methods" patented technology

Learning methods may be defined as any interventions deliberately undertaken to help the learning process at individual, team or organisational level. In rapidly changing business environments, employees need to be able to continue learning and adapting their capabilities to support the organisation’s strategy.

Resolving and merging duplicate records using machine learning

According to various embodiments of the present invention, an automated technique is implemented for resolving and merging fields accurately and reliably, given a set of duplicated records that represents a same entity. In at least one embodiment, a system is implemented that uses a machine learning (ML) method, to train a model from training data, and to learn from users how to efficiently resolve and merge fields. In at least one embodiment, the method of the present invention builds feature vectors as input for its ML method. In at least one embodiment, the system and method of the present invention apply Hierarchical Based Sequencing (HBS) and / or Multiple Output Relaxation (MOR) models in resolving and merging fields. Training data for the ML method can come from any suitable source or combination of sources.
Owner:XANT INC

Weak hypothesis generation apparatus and method, learning apparatus and method, detection apparatus and method, facial expression learning apparatus and method, facial expression recognition apparatus and method, and robot apparatus

A facial expression recognition system that uses a face detection apparatus realizing efficient learning and high-speed detection processing based on ensemble learning when detecting an area representing a detection target and that is robust against shifts of face position included in images and capable of highly accurate expression recognition, and a learning method for the system, are provided. When learning data to be used by the face detection apparatus by Adaboost, processing to select high-performance weak hypotheses from all weak hypotheses, then generate new weak hypotheses from these high-performance weak hypotheses on the basis of statistical characteristics, and select one weak hypothesis having the highest discrimination performance from these weak hypotheses, is repeated to sequentially generate a weak hypothesis, and a final hypothesis is thus acquired. In detection, using an abort threshold value that has been learned in advance, whether provided data can be obviously judged as a non-face is determined every time one weak hypothesis outputs the result of discrimination. If it can be judged so, processing is aborted. A predetermined Gabor filter is selected from the detected face image by an Adaboost technique, and a support vector for only a feature quantity extracted by the selected filter is learned, thus performing expression recognition.
Owner:SAN DIEGO UNIV OF CALIFORNIA +1

Deep and reinforcement learning-based real-time online path planning method of

The present invention provides a deep and reinforcement learning-based real-time online path planning method. According to the method, the high-level semantic information of an image is obtained through using a deep learning method, the path planning of the end-to-end real-time scenes of an environment can be completed through using a reinforcement learning method. In a training process, image information collected in the environment is brought into a scene analysis network as a current state, so that an analytical result can be obtained; the analytical result is inputted into a designed deep cyclic neural network; and the decision-making action of each step of an intelligent body in a specific scene can be obtained through training, so that an optimal complete path can be obtained. In an actual application process, image information collected by a camera is inputted into a trained deep and reinforcement learning network, so that the direction information of the walking of the intelligent body can be obtained. With the method of the invention, obtained image information can be utilized to the greatest extent under a premise that the robustness of the method is ensured and the method slightly depends on the environment, and real-time scene walking information path planning can be realized.
Owner:NORTHWESTERN POLYTECHNICAL UNIV

Method and system for training in an adaptive manner

A learning method and system are disclosed that assess a learner's understanding of the subject matter and the learner's preferred learning style by presenting and reviewing the information in various types of teaching strategies and then selecting the teaching strategies in which the student learns best. As the student responds to questions presented during the course, a learning bias model is developed for the learner based on which teaching styles provide the best level of comprehension for the learner and then presents concepts from the course within those learning strategies most suitable to the student.
Owner:POINTECAST CORP

Configurable Machine Learning Method Selection and Parameter Optimization System and Method

A system and method for selecting a machine learning method and optimizing the parameters that control its behavior including receiving data; determining, using one or more processors, a first candidate machine learning method; tuning, using one or more processors, one or more parameters of the first candidate machine learning method; determining, using one or more processors, that the first candidate machine learning method and a first parameter configuration for the first candidate machine learning method are the best based on a measure of fitness subsequent to satisfaction of a stop condition; and outputting, using one or more processors, the first candidate machine learning method and the first parameter configuration for the first candidate machine learning method.
Owner:SKYTREE INC

System and method for predicting power plant operational parameters utilizing artificial neural network deep learning methodologies

A system and method of predicting future power plant operations is based upon an artificial neural network model including one or more hidden layers. The artificial neural network is developed (and trained) to build a model that is able to predict future time series values of a specific power plant operation parameter based on prior values. By accurately predicting the future values of the time series, power plant personnel are able to schedule future events in a cost-efficient, timely manner. The scheduled events may include providing an inventory of replacement parts, determining a proper number of turbines required to meet a predicted demand, determining the best time to perform maintenance on a turbine, etc. The inclusion of one or more hidden layers in the neural network model creates a prediction that is able to follow trends in the time series data, without overfitting.
Owner:SIEMENS AG

Stochastic spiking network learning apparatus and methods

Generalized learning rules may be implemented. A framework may be used to enable adaptive spiking neuron signal processing system to flexibly combine different learning rules (supervised, unsupervised, reinforcement learning) with different methods (online or batch learning). The generalized learning framework may employ time-averaged performance function as the learning measure thereby enabling modular architecture where learning tasks are separated from control tasks, so that changes in one of the modules do not necessitate changes within the other. Separation of learning tasks from the control tasks implementations may allow dynamic reconfiguration of the learning block in response to a task change or learning method change in real time. The generalized spiking neuron learning apparatus may be capable of implementing several learning rules concurrently based on the desired control application and without requiring users to explicitly identify the required learning rule composition for that task.
Owner:BRAIN CORP

Dynamic learning method and adaptive normal behavior profile (NBP) architecture for providing fast protection of enterprise applications

A dynamic learning method and an adaptive normal behavior profile (NBP) architecture for providing fast protection of enterprise applications are disclosed. The adaptive NBP architecture includes a plurality of profile items. Each profile item includes a plurality of profile properties holding the descriptive values of the respective item. An application-level security system can identify and prevent attacks targeted at enterprise applications by matching application events against at least a single profile item in the adaptive NBP.
Owner:IMPERVA

Transfer of knowledge from a human skilled worker to an expert machine - the learning process

A learning environment and method which is a first milestone to an expert machine that implements the master-slave robotic concept. The present invention is of a learning environment and method for teaching the master expert machine by a skilled worker that transfers his professional knowledge to the master expert machine in the form of elementary motions and subdivided tasks. The present invention further provides a stand alone learning environment, where a human wearing one or two innovative gloves equipped with 3D feeling sensors transfers a task performing knowledge to a robot in a different learning process than the Master-Slave learning concept. The 3D force\torque, displacement, velocity\acceleration and joint forces are recorded during the knowledge transfer in the learning environment by a computerized processing unit that prepares the acquired data for mathematical transformations for transmitting commands to the motors of a robot. The objective of the new robotic learning method is a learning process that will pave the way to a robot with a “human-like” tactile sensitivity, to be applied to material handling, or man / machine interaction.
Owner:TAIROB

CAD (computer-aided decision) support for medical imaging using machine learning to adapt CAD process with knowledge collected during routine use of CAD system

CAD (computer-aided decision) support systems, methods and tools for medical imaging are provided, which use machine learning classification for automated detection and marking of regions of interest in medical images. Machine learning methods are used for adapting / optimizing a CAD process by seamlessly incorporating physician knowledge into the CAD process using training data that is obtained during routine use of the CAD system.
Owner:SIEMENS HEALTHCARE GMBH

Medical information extraction system and method based on depth learning and distributed semantic features

ActiveCN105894088AAvoid floating point overflow problemsHigh precisionNeural learning methodsNerve networkStudy methods
he invention discloses a medical information extraction system and method based on depth learning and distributed semantic features. The system is composed of a pretreatment module, a linguistic-model-based word vector training module, a massive medical knowledge base reinforced learning module, and a depth-artificial-neural-network-based medical term entity identification module. With a depth learning method, generation of the probability of a linguistic model is used as an optimization objective; and a primary word vector is trained by using medical text big data; on the basis of the massive medical knowledge base, a second depth artificial neural network is trained, and the massive knowledge base is combined to the feature leaning process of depth learning based on depth reinforced learning, so that distributed semantic features for the medical field are obtained; and then Chinese medical term entity identification is carried out by using the depth learning method based on the optimized statement-level maximum likelihood probability. Therefore, the word vector is generated by using lots of unmarked linguistic data, so that the tedious feature selection and optimization adjustment process during medical natural language process can be avoided.
Owner:神州医疗科技股份有限公司 +1

Relevance recognition for a human machine dialog system contextual question answering based on a normalization of the length of the user input

Disclosed are systems, methods and computer-readable media for controlling a computing device to provide contextual responses to user inputs. The method comprises receiving a user input, generating a set of features characterizing an association between the user input and a conversation context based on at least a semantic and syntactic analysis of user inputs and system responses, determining with a data-driven machine learning approach whether the user input begins a new topic or is associated with a previous conversation context and if the received question is associated with the existing topic, then generating a response to the user input using information associated with the user input and any previous user input associated with the existing topic.
Owner:NUANCE COMM INC

System and method for applying ranking svm in query relaxation

An enterprise-wide query relaxative support vector machine ranking algorithm approach provides enhanced functionality for query execution in a heterogeneous enterprise environment. Improved query results are obtained by adjusting ranking functions using machine learning methods to automatically train ranking functions. The improved query results are obtained using a list of document-query pairs that are modeled as a binary classification training problem, combination function which requires ranking and learning functions to be implemented representing document attributes and metadata utilizing query relaxation techniques and adjusted ranking functions. Machine learning methods implement user feedback to automatically train ranking functions.
Owner:ORACLE INT CORP

Learning Device, Learning Method, and Program

A learning device includes: a plurality of learning modules, each of which performs update learning to update a plurality of model parameters of a pattern learning model that learns a pattern using input data; model parameter sharing means for causing two or more learning modules from among the plurality of learning modules to share the model parameters; and sharing strength updating means for updating sharing strengths between the learning modules so as to minimize learning errors when the plurality of model parameters are updated by the update learning.
Owner:SONY CORP

Dictionary learning method and device using the same, input method and user terminal device using the same

This invention provides a dictionary learning method, said method comprising the steps of: learning a lexicon and a Statistical Language Model from an untagged corpus; integrating the lexicon, the Statistical Language Mode and subsidiary word encoding information into a small size dictionary. And this invention also provides an input method on a user terminal device using the dictionary with Part-of-Speech information and a Part-of-Speech Bi-gram Model added, and a user terminal device using the same. Therefore, sentence level prediction and word level prediction can be given by the user terminal device and the input is speeded up by using the dictionary which is searched by a Patricia Tree index of a dictionary index.
Owner:NEC (CHINA) CO LTD

Generative machine learning systems for drug design

In various embodiments, the systems and methods described herein relate to generative models. The generative models may be trained using machine learning approaches, with training sets comprising chemical compounds and biological or chemical information that relate to the chemical compounds. Deep learning architectures may be used. In various embodiments, the generative models are used to generate chemical compounds that have desired characteristics, e.g. activity against a selected target. The generative models may be used to generate chemical compounds that satisfy multiple requirements.
Owner:PREFERRED NETWORKS INC

Computer-implemented learning method and apparatus

InactiveUS20100003659A1Maintaining motivation in learningBroaden their knowledgeReadingElectrical appliancesAdaptive learningStudy methods
A computer-implemented adaptive learning method is disclosed. The method is intended for performance within the context of a task being carried out by a user. At least one of a sequence of elements presented to the user as part of the task is designated as a learning item. A learning object is selected in dependence upon the designated learning item, information relating to previous performance of the learning method in relation to the user, and a predetermined scheme devised to manage an overall learning process for the user. Presentation of the selected learning object to the user is intended to advance the user's knowledge of the designated learning item in some way. Once the learning object has been presented to the user, the information is updated in dependence upon the presented learning object and / or how the user interacts with or responds to the presented learning object.
Owner:SHARP KK

Multi-attribute specification of preferences about people, priorities and privacy for guiding messaging and communications

The present invention relates to a system and methodology to facilitate multiattribute adjustments and control associated with messages and other communications and informational items that are directed to a user via automated systems. An interface, specification language, and controls are provided for defining a plurality of variously configured groups that may attempt to communicate respective items. Controls include the specification of priorities and preferences as well as the modification of priorities and preferences that have been learned from training sets via machine learning methods. The system provides both a means for assessing parameters used in the control of messaging and communications and for the inspection and modification of parameters that have been learned autonomously.
Owner:MICROSOFT TECH LICENSING LLC

Deep convolution neural network training method and device

The present invention relates to the field of deep learning techniques, in particular to a deep convolution neural network training method and a device. The deep convolution neural network training method and the device comprise the steps of a, pretraining the DCNN on a large scale data set, and pruning the DCNN; b, performing the migration learning on the pruned DCNN; c, performing the model compression and the pruning on the migrated DCNN with the small-scale target data set, In the process of migrating learning of large-scale source data set to small-scale target data set, the model compression and the pruning are performed on the DCNN by the migration learning method and the advantages of model compression technology, so as to improve the migration learning ability to reduce the risk of overfitting and the deployment difficulty on the small-scale target data set and improve the prediction ability of the model on the target data set.
Owner:SHENZHEN INST OF ADVANCED TECH

Computer-aided group-learning methods and systems

Providing methods and systems for a computer-aided group-learning environment, where a number of users can interact and work on a subject together. The system and method can monitor and analyze users' inputs. The analysis process can identify a user's performance on the subject, and can understand some of the user's traits, such as confidence level and learning attitude. The system can include an interaction controller, which sets a duration of time for the users to communicate in a dialogue environment. Working on the subject in a group and working alone can be intertwined. For example, the users first work on the materials generated individually, and then solve the problem together in a dialogue environment. During the dialogue session, the interaction controller can provide hints to the users. The system can also include a user registry, which restricts the users who can use the embodiment to work on the subject. The registry can receive potential user's characteristics to determine whether such user may be allowed to join the existing users to work on the subject. The registry can also access a summarized profile of the existing users to help the potential user make joining decisions. The system can also include a notepad for a user to take notes. The interaction controller can also guide the user to take notes.
Owner:FAI HO CHI +1

Method to indentify anomalous data using cascaded K-Means clustering and an ID3 decision tree

The invention is a computer implemented technique for id entifying anomalous data in a data set. The method uses cascaded k-Means clustering and the ID3 decision tree learning methods to characterize a training data set having data points with known characterization. The k-Means clustering method first partitions the training instances into k clusters using Euclidean distance similarity. On each training cluster, representing a density region of normal or anomaly instances, the invention builds an ID3 decision tree. The decision tree on each cluster refines the decision boundaries by learning the sub-groups within the cluster. A test data point is then subjected to the clustering and decision trees constructed form the training instances. To obtain a final decision on classification, the decisions of the k-Means and ID3 methods are combined using rules: (1) the Nearest-neighbor rule, and (2) the Nearest-consensus rule.
Owner:LOUISIANA TECH RES CORP

Train operation fault automatic detection system and method based on binocular stereoscopic vision

The invention discloses a train operation fault automatic detection system and method based on binocular stereoscopic vision, and the method comprises the steps: collecting left and right camera images of different parts of a train based on a binocular stereoscopic vision sensor; achieving the synchronous precise positioning of various types of target regions where faults are liable to happen based on the deep learning theory of a multi-layer convolution neural network or a conventional machine learning method through combining with the left and right image consistency fault (no-fault) constraint of the same part; carrying out the preliminary fault classification and recognition of a positioning region; achieving the synchronous precise positioning of multiple parts in a non-fault region through combining with the priori information of the number of parts in the target regions; carrying out the feature point matching of the left and right images of the same part through employing the technology of binocular stereoscopic vision, achieving the three-dimensional reconstruction, calculating a key size, and carrying out the quantitative description of fine faults and gradually changing hidden faults, such as loosening or playing. The method achieves the synchronous precise detection of the deformation, displacement and falling faults of all big parts of the train, or carries out the three-dimensional quantitative description of the fine and gradually changing hidden troubles, and is more complete, timely and accurate.
Owner:BEIHANG UNIV

Automated segmentation of organ chambers using deep learning methods from medical imaging

Systems and methods are disclosed for automatically segmenting a heart chamber from medical images of a patient. The system may include one or more hardware processors configured to: obtain image data including at least a representation of the patient's heart; obtain a region of interest from the image data; organize the region of interest into an input vector; apply the input vector through a trained graph; obtain an output vector representing a refined region of interest corresponding to the heart based on the application of the input vector through the trained graph; apply a deformable model on the obtained output vector representing the refined region of interest; and identify a segment of a heart chamber from the application of the deformable model on the obtained output vector.
Owner:RGT UNIV OF CALIFORNIA

Method of providing and administering a web-based personal financial management course

A method of learning that uses a learning assessment process to determine an optimal way in which to deliver an educational course that best suits the optimal learning style of a specific individual. The method of learning may, in a preferred embodiment, be used to provide and administer a web-based personal financial management course.
Owner:DANCY EDWARDS GLYNDA P

Automated license plate recognition system and method using human-in-the-loop based adaptive learning

An automated license plate recognition (ALPR) system and method using a human-in-the-loop based adaptive learning approach. One or more images with respect to an automotive vehicle can be segmented in order to determine a license plate of the automotive vehicle within a scene. An optical character recognition (OCR) engine loaded with an OCR algorithm can be further adapted to determine a character sequence of the license plate based on a training data set. A confidence level with respect to the images can be generated in order to route a low confidence image to an operator for obtaining a human interpreted image. The parameters with respect to the OCR algorithm can be adjusted based on the human interpreted image and the actual image of the license plate. A license plate design can be then incorporated into the OCR engine in order to automate the process of recognizing the license plate with respect to the automotive vehicle in a wide range of transportation related applications.
Owner:CONDUENT BUSINESS SERVICES LLC

Tomographic image reconstruction via machine learning

Tomographic / tomosynthetic image reconstruction systems and methods in the framework of machine learning, such as deep learning, are provided. A machine learning algorithm can be used to obtain an improved tomographic image from raw data, processed data, or a preliminarily reconstructed intermediate image for biomedical imaging or any other imaging purpose. In certain cases, a single, conventional, non-deep-learning algorithm can be used on raw imaging data to obtain an initial image, and then a deep learning algorithm can be used on the initial image to obtain a final reconstructed image. All machine learning methods and systems for tomographic image reconstruction are covered, except for use of a single shallow network (three layers or less) for image reconstruction.
Owner:RENESSELAER POLYTECHNIC INST
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