5069results about How to "Improve classification accuracy" patented technology

A system and method for distinguishing human input events from malware-generated events includes one or more central processing units (CPUs), one or more input devices and memory. The memory includes program code that when executed by the CPU causes the CPU to obtain a first set of input events from a user utilizing the input device. The first input events are used to obtain or derive a feature indicative of the user, such as a multi-dimensional feature vector as provided by a support vector machine. Second input events are then obtained, and the second input events are classified against the feature to determine if either the user or malware initiated the second input events.

The invention discloses an attention CNNs and CCR-based text sentiment analysis method and belongs to the field of natural language processing. The method comprises the following steps of 1, training a semantic word vector and a sentiment word vector by utilizing original text data and performing dictionary word vector establishment by utilizing a collected sentiment dictionary; 2, capturing context semantics of words by utilizing a long-short-term memory (LSTM) network to eliminate ambiguity; 3, extracting local features of a text in combination with convolution kernels with different filtering lengths by utilizing a convolutional neural network; 4, extracting global features by utilizing three different attention mechanisms; 5, performing artificial feature extraction on the original text data; 6, training a multimodal uniform regression target function by utilizing the local features, the global features and artificial features; and 7, performing sentiment polarity prediction by utilizing a multimodal uniform regression prediction method. Compared with a method adopting a single word vector, a method only extracting the local features of the text, or the like, the text sentiment analysis method can further improve the sentiment classification precision.

The invention discloses a high-efficiency SVM active half-supervision learning algorithm. The algorithm comprises: (1), training an initial SVM classifier f<SVM><0>; (2), determining whether the f<SVM><0> satisfies a learning termination condition, and if not, skipping to step (3); (3), performing prediction marking on unmarked samples Us by use of the f<SVM><0>; (4), performing Tri-learning based half-supervision learning/QBC-based active learning on samples whose prediction mark confidence are greater than/smaller than a threshold in the Us, and adding the samples selected in the half-supervision learning/active learning to a marked training sample set; (5), training a f<SVM><k> on the updated marked training sample; and (6), repeating step (2) until the SVM classifier satisfies the termination condition of the active learning. The algorithm provided by the invention has the following advantages: during an SVM training learning process, according to the learning process, the samples which best facilitate classifier performance are autonomously selected for training the classifier, after these samples are added to the tainting set, the accuracy of classifying the unmarked samples through the semi-supervision learning is improved to the maximum degree, and the SVM classification precision is enhanced.

The invention discloses a zero sample image classification method based on combination of a variational autocoder and an adversarial network. Samples of a known category are input during model training; category mapping of samples of a training set serves as a condition for guidance; the network is subjected to back propagation of optimization parameters through five loss functions of reconstruction loss, generation loss, discrimination loss, divergence loss and classification loss; pseudo-samples of a corresponding unknown category are generated through guidance of category mapping of the unknown category; and a pseudo-sample training classifier is used for testing on the samples of the unknown category. The high-quality samples beneficial to image classification are generated through theguidance of the category mapping, so that the problem of lack of the training samples of the unknown category in a zero sample scene is solved; and zero sample learning is converted into supervised learning in traditional machine learning, so that the classification accuracy of traditional zero sample learning is improved, the classification accuracy is obviously improved in generalized zero sample learning, and an idea for efficiently generating the samples to improve the classification accuracy is provided for the zero sample learning.

The invention provides an SAR image terrain classification method based on a depth RBF network. The method mainly solves the problem of the prior art that the accuracy of classification is low. The method comprises the steps of (1) extracting the texton features of an SAR image; (2) training the texton features of the SAR image through a first-layer RBF neural network of the depth RBF network to obtain the advanced features of the image; (3) training the advanced features through a second-layer sparse autocoder network SAE of the depth RBF network to obtain more advanced features of the image; (4) training the more advanced features through a third-layer RBF neural network of the depth RBF network to obtain the terrain classification features of the image; (5) comparing the terrain classification features of an image test sample with a test sample label, adjusting the parameters of each layer of the depth RBF network, and obtaining an optimal test classification accuracy. The method is high in classification accuracy and can be used for complicated image classification.

The invention provides an MRI (Magnetic Resonance Imaging) brain tumor localization and intratumoral segmentation method based on a deep cascaded convolution network, which comprises the steps of building a deep cascaded convolution network segmentation model; performing model training and parameter optimization; and carrying out fast localization and intratumoral segmentation on a multi-modal MRIbrain tumor. According to the MRI brain tumor localization and intratumoral segmentation method provided by the invention based on the deep cascaded convolution network, a deep cascaded hybrid neuralnetwork formed by a full convolution neural network and a classified convolution neural network is constructed, the segmentation process is divided into a complete tumor region localization phase andan intratumoral sub-region localization phase, and hierarchical MRI brain tumor fast and accurate localization and intratumoral sub-region segmentation are realized. Firstly, the complete tumor region is localized from an MRI image by adopting a full convolution network method, and then the complete tumor is further divided into an edema region, a non-enhanced tumor region, an enhanced tumor region and a necrosis region by adopting an image classification method, and accurate localization for the multi-modal MRI brain tumor and fast and accurate segmentation for the intratumoral sub-regions are realized.

The invention provides a human body behavior identification method based on an inertial sensor. The method comprises the steps of acquiring human body behavior data of testees by means of the inertial sensor, conducting sliding window segmentation on the acquired human body behavior data, conducting feature extraction on a triaxial accelerated speed subsequence and a triaxial angular speed subsequence which are obtained after sliding window segmentation, conducting feature fusion on a feature vector to form a sample set of the human body behaviors of the testees, conducting feature selection on the sample set by means of the least correlated maximum redundant algorithm and the Bayes regularization sparse polynomial logistic regression algorithm, obtaining the classification feature vectors of all human body behaviors of all the testees, obtaining a classifier model of each human body behavior by means of a fuzzy least square support vector machine, and obtaining a human body behavior identification result after the human body behavior data are tested by means of the fuzzy least square support vector machine. By means of the human body behavior identification method, self-adaptation and identification efficiency can be improved.

A solution of the invention discloses a multi-feature-fusion Chines-text classification method based on Attention neural network, and belongs to the field of natural language processing. In order to further improve accuracy of Chinese-text classification, the method fully exploits features of text data under three different sizes of convolution kernel granularity through fusing three CNN paths; interconnections among the text data are manifested through fusing an LSTM path; and in particular, relatively important data features are enabled to play a greater role in a Chinese-text class recognition process through merging a provided Attention algorithm model, and thus recognition ability of a model on Chinese text classes is improved. Experiment results show that compared with a CNN model, an LSTM structure model and a combined model of the two parts under the same experiment conditions, the model provided by the invention is significantly improved in Chinese-text classification accuracy, and can be better applied to the Chinese-text classification field with high requirements on the classification accuracy.

Mobile device (102) comprising a number of sensing entities (230) for obtaining data indicative of the context of the mobile device and/or user thereof, a feature determination logic (230) for determining a plurality of representative feature values utilizing the data, and a context recognition logic (228) including an adaptive linear classifier (234), configured to map, during a classification action, the plurality of feature values to a context class, wherein the classifier is further configured to adapt (236) the classification logic thereof on the basis of the feature values and feedback information by the user of the mobile device. A method to be performed by the mobile device is presented.

The invention discloses an attention mechanism relationship comparison network model method. An attention relationship comparison network model for small sample learning under a small amount of labeled sample data is constructed. Based on a relational network architecture, the model is divided into a feature coding part, a feature combination part and a relational coding part, the feature coding module is used for extracting image feature information, and the feature combination part is used for recombining the extracted query image feature information with the training image feature information of each group to form a new combined feature map. The relation encoding module performs nonlinear metric learning of the network; by introducing an attention mechanism and a spectrum normalizationmethod into an end-to-end deep convolutional neural network model, the model has higher classification accuracy under the condition of small sample learning, the stability of a final training result of the model is improved, and the image classification accuracy of an existing model in small sample learning is improved.

The invention, which belongs to the technical field of remote-sensing image classification, relates to a multi-mode-characteristic-fusion-based remote-sensing image classification method. Characteristics of at least two modes are extracted; the obtained characteristics of the modes are inputted into an RBM model to carry out fusion to obtain a combined expression of characteristics of the modes; and according to the combined expression, type estimation is carried out on each super-pixel area, thereby realizing remote-sensing image classification. According to the invention, the characteristics, including a superficial-layer mode characteristic and a deep-layer mode characteristic, of various modes are combined by the RBM model to obtain the corresponding combined expression, wherein the combined expression not only includes the layer expression of the remote-sensing image deep-layer mode characteristic but also includes external visible similarity of the superficial-layer mode characteristic. Therefore, the distinguishing capability is high and the classification precision of remote-sensing images is improved.

The invention provides an image quality evaluating method based on a support vector machine. The method comprises the following steps: first, a preprocessed image sample is selected and extracted according to characteristic value, a processed sample set is respectively divided into a training set and a testing set; secondly, the training set is used for training the support vector machine, the number of the support vector machine is ensured according to a certain level which is required by a system, thus ensuring each support vector machine to be trained, wherein, an input sample is the characteristic value of the image and an output sample is the level of the image quality; thirdly, the trained support vector machine is used for adjusting and optimizing correlation parameters with the testing set and determining the parameter of the decision function of the optimal hyperplane of the support vector machine model; and finally, the support vector machine model which is trained and optimized is used for evaluating the quality level of the image sample. The invention has the advantages of little required sample, fast arithmetic speed, high precision, good performance, strong popularization, etc.

The invention relates to a hierarchical classification method for internet flow, comprising the following steps: firstly, coarse grained classification is conducted as follows: a flow statistical property method based on machine learning is adopted for quickly classifying network flow, the network flow is divided into applied categories with different characteristics, if the flow distinguished by the coarse grained classification needs to be used, the flow can be output directly, and if the flow needs further classification, next step is conducted; and secondly, fine grained classification is conducted as follows: classification is further conducted on the applied categories distinguished by the coarse grained classification, the network flow can be intelligently, accurately and effectively identified in real time, good integrity and expandability are realized, and the requirements on the internet flow classification with different application targets and classification grain size can be met.

The present invention discloses an integrated support vector machine mixed intelligent diagnosis method of machine failure. Said method includes the following steps: respectively adopting and lifting small wave packet, utilizing frequency band and empirical mode decomposition process to decompose vibration signal according to the eigen mode component and extract time domain statistical character of decomposed signal to form total characteristic set; providing characteristic distance evaluation technique and characteristic evaluation index; utilizing said characteristic evaluation index to select most sensitive characteristic set from the total characteristic set; using said most sensitive characteristic set as diagnosis characteristics and creating integrated support vector machine mixed intelligent diagnosis model so as to implement intelligent diagnosis of machine failure state.

The invention discloses a web-based text classification mining system and a web-based text classification mining method. The system mainly comprises a text pre-processing module, a word segmentation processing module and a classification algorithm module, wherein the text pre-processing module is used for automatically screening specific information from texts to be tested, pre-processing the specific information, and filtering out irrelevant information to effectively represent the texts; the word segmentation processing module is used for carrying out word segmentation on the texts, finding attributes/attributive words of each text, and making preparation for selection of characteristic words; and the classification algorithm module is used for carrying out characteristic selection to obtain an optimum characteristic sub-set, or finding corresponding probabilities according to data which is provided by a file of a training result, comparing the corresponding probabilities to obtain the type of the maximum probability, drawing a conclusion and storing the conclusion in the file finally. The system overcomes the shortcoming of conditional independence assumption of a naive Bayes algorithm by using a hypertext markup language (HTML) tag weight, improves a classifier and can improve the recall ratio and precision ratio of data mining.

The invention provides a method of constructing a text classification model. The method comprises the steps of: constructing a training sample set according to structure features of characters, wordsand sentences of text information, wherein each piece of sample data in the training sample set corresponds to a feature matrix A of a piece of text information about the words and a feature matrix Babout the characters and a category vector O corresponding to the piece of text information, and the dimension number of O is the same as a category number; and using the feature matrix A about the words and the feature matrix B about the characters in the training matrix set as input and the corresponding category vector O as output to train a deep learning model to obtain the text classificationmodel. Classification is carried out according to the classification model constructed by the method, an accuracy rate of text classification can be improved, and the method is particularly suitablefor use in short-text classification.

In one example, a method includes receiving a feature vector that characterizes a call history for a telephone network subscriber, wherein the feature vector comprises respective categorical values for one or more categorical features and respective continuous values for one or more continuous features, and applying, to the categorical values, a first algorithm to determine a categorical score for the feature vector. The example method further includes applying, to the continuous values, an isolation forest algorithm to determine a continuous score for the feature vector, and outputting, in response to determining at least one of the categorical score for the feature vector and the continuous score for the feature vector indicate the feature vector is anomalous, an indication that the feature vector is anomalous.

A call center includes a message playback unit for playing back a prerecorded greeting during an outgoing call while a call classifier unit within the call center processes an audible signal received from a remote party location. A call processing unit places a call to a remote party location via a communication network to attempt to elicit a desired response from the remote party. The call classifier then processes an audible signal received from the remote party location to determine whether or not the call was answered by a live party. The prerecorded greeting is then played while the call classifier is simultaneously processing the audible signal. If the call classifier determines that the call was answered by a live party, the call processing unit completes a talk path between the remote party location and a local agent at the call center who handles the remainder of the call. The call processing unit will normally wait until the greeting has ended to complete the talk path. If the call classifier determines that the call was not answered by a live party, the call processing unit will terminate the call.

The invention relates to a recognition method of digital music emotion, belonging to the field of computer pattern recognition; the recognition method solves the problem that the existing recognition method of digital music emotion can not recognize sampling-based digital music format, the sorting technology based on a multi-class support vector machine is adopted, acoustic characteristic parameters and music theory characteristic parameters are combined, so as to carry out emotion recognition of digital music; the recognition method comprises the following steps: (1) pretreatment; (2) characteristic extraction; (3) training the multi-class support vector machine; (4) recognition. The music emotion is classified into happiness, impassion, sadness and relaxation, the emotion recognition is carried out based on a sampling-based digital music format file, the common acoustic characteristics in the speech recognition field are not only extracted, and a series of music theory characteristics are extracted according to the theory characteristics of music; meanwhile, the sorting method based on the support vector machine is adopted, the leaning speed is rapid, the sorting precision ratio is high and the recognition efficiency is improved.

The invention discloses a firework identification method and a firework identification system based on deep learning of an image. The firework identification method comprises the following steps of step 1, acquiring a label-free sample image set and a label sample image set; step 2, obtaining a label-free training data set and a label training data set; step 3, performing whitening preliminary processing on training data; step 4, based on the label-free training data subjected to the whitening preliminary processing, constructing a deep neutral network based on sparse self coding by adopting unsupervised learning, and extracting a basic image feature set of the label-free training data; step 5, convolving basic image features and pooling image data; step 6, training a Softmax classifier based on the convolved and pooled label training data set; step 7, inputting the convolved and pooled images to be identified into the trained Softmax classifier to obtain the identification result. According to the firework identification method and the firework identification system disclosed by the invention, the visual identification rate of fireworks and a similar object can be effectively improved, and automatic identification with higher precision for the fireworks can be realized.

The invention discloses a multilayer convolutional neural network-based power transmission line equipment image defect detection method and system. The method comprises the steps of performing modularpreprocessing on an original training set image, and inputting the image subjected to the modular preprocessing into a multilayer convolutional neural network model for performing training; for the image subjected to the modular preprocessing, selecting different training set sizes and training parameters, repeating the step 2 for performing multi-time experiments, performing comparative analysison classification accuracy and efficiency, selecting out optimal training parameters and performing storage; and performing filtration through a judger formed based on an environment structure and priori knowledge, and correcting false detection and missing detection information to obtain a final image defect detection result. The method and the system have the beneficial effects that a machine learning model with multiple hidden layers is built, and valuable expressive features are learnt from a large amount of data, so that the classification or prediction accuracy is improved.