48 results about "Hybrid learning" patented technology

Aspects of the present invention include object detection training systems and methods and using object detection systems and methods that have been trained. Embodiments presented herein include hybrid learning approaches that combine global classification and local adaptations, which automatically adjust model complexity according to data distribution. Embodiments of the present invention automatically determine model complexity of the local learning algorithm according to the distribution of ambiguous samples. And, embodiments of the local adaptation from global classifier avoid the common under-training problem for local classifier.

The invention disclosed relates to a learning system and related methods for providing training and continuing education to members of an organization's supply-chain workforce. A learning system utilizes a blended learning approach that matches various educational delivery techniques to the appropriate learning materials, time constraints, and skill level of the end user. The system is simultaneously accessible to multiple organizations in order to provide uniformity of learning among a wide variety of participants within the supply-chain workforce, while remaining adaptable to the unique learning needs of each organization. Thus, the invention captures the benefits of distance learning techniques while minimizing the isolationist learning environment typically associated with those leaning delivery methods. Furthermore, the invention seeks to capture the benefits associated with traditional learning delivery methods such as team-oriented learning and personal instructor feedback.

A blended learning solution is defined by determining degrees of elements in learning zones. The zones are defined by the types of interactions of the learners. Both formal and informal learning is defined for each zone. A specialized circular display is constructed to depict the optimal blended learning solution.

Aspects of the present invention include object detection training systems and methods and using object detection systems and methods that have been trained. Embodiments presented herein include hybrid learning approaches that combine global classification and local adaptations, which automatically adjust model complexity according to data distribution. Embodiments of the present invention automatically determine model complexity of the local learning algorithm according to the distribution of ambiguous samples. And, embodiments of the local adaptation from global classifier avoid the common under-training problem for local classifier.

The invention belongs to the field of behavior recognition, particularly relates to a behavior feature extraction method, system based on space-time frequency domain hybrid learning, and a device, andaims to solve the problem of low skeleton behavior feature extraction precision. The method comprises the steps of obtaining a video behavior sequence based on a skeleton, and extracting a time-spacedomain behavior feature map through converting a network; inputting the time-space domain behavior feature map into a frequency domain attention network, performing frequency selection, inverting toa time-space domain, and adding the obtained behavior feature map to the time-space domain behavior feature map; synchronously performing local and non-local reasoning, and performing high-level localreasoning; and globally pooling the time-space domain behavior feature map obtained through reasoning to obtain the behavior feature vector of the video behavior sequence. The method can be applied to behavior classification, behavior detection and the like. According to the method, an effective frequency mode is adaptively selected in a frequency domain, a network with local affinity fields andnon-local affinity fields is adopted in a time-space domain for space-time reasoning, local details and non-local semantic information can be synchronously mined, and therefore the behavior recognition precision is effectively improved.

The invention relates to a new sentiment classification model based on hybrid learning. In the first stage, an improved dictionary classification method is used for calculating emotion scores on a whole data set, and data with extremely high scores or extremely low scores are directly marked; in the second stage, the rest is used to calculate the emotion score based on the emotion dictionary and the BI-GRU fusion model, and the two-stage hybrid framework enables the method to be effectively applied to emotion classification. Experiments show that a single model is unsatisfactory in emotion classification effect under various complex contexts, high in difficulty and low in precision, and error preference of the single model can be effectively improved by adopting a multi-model fusion method, so that the classification effect is improved.

The invention discloses a wireless federated learning method and device, and the method comprises the steps: enabling a distributed learning user based on air computing and a centralized learning user based on non-orthogonal multiple access to share uplink spectrum resources through the concurrent transmission assisted by a dual-function intelligent metasurface, the local data and the model parameters are sent to a base station at the same time for mixed learning; a base station carries out signal decoding on centralized users based on non-orthogonal multiple access so as to obtain local data of each centralized learning user, and the local data are used for calculating model parameters of the centralized learning users; then the base station obtains average model parameters of federated learning users by using serial interference cancellation and air computing technologies; the base station updates a global model by combining the two types of model parameters; and then the base station issues the global model to all federated learning users to carry out the next round of learning until the global model converges or reaches the maximum number of iterations. According to the method, the communication overhead and the transmission delay can be remarkably reduced, and better learning performance can be obtained.

The invention provides a hybrid learning evaluation system and method based on the cloud computing and the Internet of things technology and belongs to the field of computational applied science. In terms of process evaluation for supporting blended learning, the conventional learning systems fail to provide flexible process evaluation for different subjects in a blended learning mode. The system provided by the invention comprises five modules, which are a behavior collection module, an evaluation method setting module, a score input module, a score calculating module and a score output module, realizes blending of online learning and offline learning, and realizes the process evaluation of blended learning with a higher informationization degree.

Systems and methods for ticket classification and response include labeling tickets with a ticket classifier that assigns a ticket label and an associated confidence score to each ticket. Tickets are clustered according to semantic similarity to form ticket clusters. A template associated with each ticket cluster is determined. Templates and the respective ticket clusters are clustered according to semantic similarity to form one or more ticket super-clusters. Tickets that have below-threshold confidence scores are labeled according to the one or more ticket super-clusters. The tickets are automatically responded to.