54 results about "Computer learning" patented technology

The invention provides an environment image multi-view-angle meaning cutting method and device, and the method comprises the following steps of: collecting multi-view-angle image data and carrying out pre-processing on the multi-view-angle image data, wherein the pre-processing includes establishing a three-dimensional coordinate system and dividing pixel points in an image into a plurality of super pixels; extracting two-dimensional characteristic vectors of the super pixels, wherein the two-dimensional characteristic vectors include an RGB value, a mean of lab color space composition, a derivation value, a bias or a peak state; extracting three-dimensional characteristic vectors of the super pixels, wherein the three-dimensional characteristic vectors include a three-dimensional point density; and substituting the two-dimensional characteristic vectors and the three-dimensional characteristic vectors into a smoothness value equation, and dividing the super pixels into a plurality of clusters. By adopting the environment image multi-view-angle meaning cutting method and device, an image identification effect in a computer system is achieved, and the problem of computer learning object classification is solved.

In a first exemplary embodiment of the present invention, an automated, computerized method is provided for processing an image. According to a feature of the present invention, the method comprises the steps of providing an image file depicting an image, in a computer memory, assembling a feature vector for the image file, the feature vector containing information regarding a likelihood that a selected pair of regions of the image file are of a same intrinsic characteristic, providing a classifier derived from a computer learning technique, computing a classification score for the selected pair of regions of the image file, as a function of the feature vector and the classifier and classifying the regions as being of the same intrinsic characteristic, as a function of the classification score.

The invention discloses a stock price fluctuation prediction system and method based on media information tensor supervised learning. According to the media information, a tensor is constructed, and a supervised learning algorithm is used to predict stock price fluctuations. The present invention has the following advantages: (1) Starting from the three-dimensional attribute information of market transaction information, investor emotional information, and media news information, it includes both quantitative and qualitative sources of information, including official news information and social media information, thus making it more comprehensive analyze the relationship between Internet media and the stock market; (2) use tensor to represent the media information space, so as to record the relationship between different dimensions of information; (3) the tensor supervised learning algorithm realizes the computer learning algorithm from the vector An extension to tensors.

A computer learning system includes a database containing one or more sample essay questions and model answers prepared in advance by qualified instructors. The system assists a user in preparing for an essay examination by presenting the user with a sample essay question and prompting the user to identify an issue raised by the essay question until the user correctly identifies an issue raised by the essay question. The user is then enabled to provide a partial response related to the identified issue and to conduct a self-evaluation as to whether the partial response is correct by comparing the partial response to a partial model answer. Once the user has completed a question, the user's complete response, which combines the user's partial responses and organizes them by issue, is displayed and the user can compare the complete response to a complete model answer, which includes the partial model answers, organized by issue.

In a first exemplary embodiment of the present invention, an automated, computerized method is provided for determining illumination flux in an image. According to a feature of the present invention, the method comprises the steps of performing a computer learning technique to determine spatio-spectral information for the images, and utilizing the spatio-spectral information to identify illumination flux.

The invention discloses a computer teaching system, which comprises a main control unit, wherein the main control unit is connected with a teacher terminal, a signal conversion module, a student terminal and a loudspeaker unit, one side of the teacher terminal is connected with a teacher computer, a projection module, a display module and a pickup module, and one side of the student terminal is connected with a student computer, a calling module, a monitoring module and a check-in module. According to the computer teaching system, the teacher can give lessons by means of the teacher computer, project teaching content by means of the projection module, and can utilize the pick-up module to explain, so as to ensure that each student can hear the knowledge explained by the teacher clearly; and the student can learn the knowledge by means of the student computer, can ask questions to the teacher and answer teacher's questions by using the calling module, and the classroom discipline of the student can be monitored by means of the monitoring module; and the computer teaching system has comprehensive functions, is stable in operation, and can effectively improve the teaching efficiency of the teacher.

The invention relates to a computer learning machine with a detachable keyboard. The computer learning machine comprises a host and the detachable keyboard. The host is provided with an accommodation cavity. The detachable keyboard is accommodated in the accommodation cavity, and comprises a first panel, a second panel and a control circuit main board, wherein the first and second panels are arranged on a shell, and are opposite to each other; the control circuit main board is arranged in the shell; the first panel comprises a full keyboard and a first touchpad; the second panel comprises direction keys and a second touchpad; the control circuit main board comprises a microprocessor, a universal serial bus (USB) data interface connected with the host and a wireless communication module; when the USB data interface is detected to be in signal connection with the host, all the operation of the second panel fails under the action of the microprocessor; and when the signal connection between the USB data interface and the host is detected to be interrupted, all the operation of the first panel fails under the action of the microprocessor, and the wireless communication module is started to be connected with the host. The keyboard can be simply and conveniently taken down and arranged, and can be used in a double-sided way.

An architecture and processes enable computer learning and developing an understanding of arbitrary natural language text through collaboration with humans in the context of joint problem solving. The architecture ingests the text and then syntactically and semantically processes the text to infer an initial understanding of the text. The initial understanding is captured in a story model of semantic and frame structures. The story model is then tested through computer generated questions that are posed to humans through interactive dialog sessions. The knowledge gleaned from the humans is used to update the story model as well as the computing system's current world model of understanding. The process is repeated for multiple stories over time, enabling the computing system to grow in knowledge and thereby understand stories of increasingly higher reading comprehension levels.

The disclosure discloses estimation of the implicit likelihoods of generative adversarial networks, and relates to the field of computer learning. The thriving of deep models and generative models provides approaches to model high dimensional distributions. Generative adversarial networks (GANs) can approximate data distributions and generate data samples from the learned data manifolds as well. Presented herein are embodiments to estimate the implicit likelihoods of GAN models. In one or more embodiments, a stable inverse function of the generator is learned with the help of a variance network of the generator. The local variance of the sample distribution may be approximated by the normalized distance in the latent space. Simulation studies and likelihood testing on data sets validate embodiments, which outperformed several baseline methods in these tasks. An embodiment was also applied to anomaly detection. Experiments show that the embodiments herein can achieve state-of-the-art anomaly detection performance.

Systems and methods are disclosed to label data collected by one instrument based on data collected by another instrument, train a computer learning model with the labeled data, and then use the trained computer learning model to estimate a condition from new unlabeled data. For example, weather-related pavement conditions may be estimated from camera images according to such systems and methods. Systems and methods are also disclosed to estimate road weather safety or hazard conditions using two different types of pavement conditions, such as road state and friction or grip coefficient, estimated from unlabeled camera images.

A method and system of training a computer learning model to improve medical care. The method and system may include training a computer learning model to obtain a predictive model that can identify success rates, potential complications, anesthesia time, and predicted life expectancy. The method and system can include inputting physiological data to generate and / or remove one or more predictive models.

The invention relates to a thinking mathematics learning method based on capability grading and a computer learning system. The learning method comprises the steps of obtaining the thinking mathematics portrait information of a student user; taking the thinking mathematics portrait information subjected to discretization processing as the input of a classifier to obtain the grade information corresponding to the student user; based on the grade information and the knowledge point learning model of the current class hour, obtaining the screening information of the student user for each knowledge point in the current class hour; and pushing the learning contents based on the screening information. Compared with the prior art, the method has the advantages of being beneficial to learning efficiency and the like.