353 results about "Bayes classifier" patented technology

The present invention is directed to generating audience analytics that includes providing a database containing a plurality of user input pattern profiles representing the group of users of terminal device, in which each user of the group is associated with one of the plurality of user input pattern profiles. A clickstream algorithm, tracking algorithm, neural network, Bayes classifier algorithm, or affinity-day part algorithm can be used to generate the user input pattern profiles. A user input pattern is detected based upon use of the terminal device by the current user and the user input pattern of the current user is dynamically matched with one of the user input pattern profiles contained in the database. The current user is identified based upon dynamic matching of the user input pattern generated by the current user with one of the user input pattern profiles. The present invention processes each user input pattern profile to identify a demographic type. A plurality of biometric behavior models are employed to identify a unique demographic type. Each user input pattern profile is compared against the plurality of biometric behavior models to match each user input pattern profile with one of the biometric behavior models such that each user input pattern profile is correlated with one demographic type. Audience analytics are then based upon the identified demographic types.

A method and apparatus as disclosed for face detection within images. A new vector, the discriminating feature analysis vector, is introduced to represent an image to be analyzed, from the DFA vector as processed using a Bayesian Classifier technique. The methodology provides a computationally efficient manner in which to detect faces with relatively low probability of error and false detection rate.

Online browsing behavior is used to predict the intent of online users dynamically. The category of online user is predicted and the prediction can be used to provide assistance to the user, if required. Such prediction is based on a combination of a Naïve's Bayes classifier and a Markov model.

The invention discloses an emotion analyzing system and an emotion analyzing method. The system comprises a language database establishing module, a data preprocessing module, a perspective sentence identifying module and an emotion tendency analyzing module, wherein the language database establishing module is used for establishing a training set needed by perspective sentence identification and emotion tendency analysis; the data preprocessing module is used for preprocessing sentences in the training set; the perspective sentence identifying module is used for performing perspective sentence identification on the preprocessed sentences by adopting a support vector machine classifier and a Bayes classifier respectively, and integrally processing results of the classifiers to obtain a final classifying result; and the emotion tendency analyzing module is used for directly classifying the preprocessed sentences into positive, negative and non-viewpoint sentences respectively on the basis of the support vector machine classifier and the Bayers classifier, and integrating the classifying results of the vector machine classifier and the Bayers classifier through an integration formula to obtain a classifying result of a current sentence. Due to the adoption of the emotion analyzing system and the emotion analyzing method, the viewpoint sentence judging and emotion tendency classifying properties of Chinese microblogs can be improved.

A no-reference estimation of video quality in streaming video is provided on a macroblock basis. Compressed video is being deployed in video in streaming and transmission applications. MB-level no-reference objective quality estimation is provided based on machine learning techniques. First the feature vectors are extracted from both the MPEG coded bitstream and the reconstructed video. Various feature extraction scenarios are proposed based on bitstream information, MB prediction error, prediction source and reconstruction intensity. The features are then modeled using both a reduced model polynomial network and a Bayes classifier. The classified features may be used as feature vector used by a client device assess the quality of received video without use of the original video as a reference.

Methods for integrating mobile electronic devices with computational methods for assigning priorities to documents are disclosed. In one embodiment, a computer-implemented method first receives a new document such as an electronic mail message. The method assigns a priority to the document, based on a text classifier such as a Bayesian classifier or a support-vector machine classifier. The method then alerts a user on an electronic device, such as a pager or a cellular phone, based on an alert criteria that can be made sensitive to information about the location, inferred task, and focus of attention of the user. Such information can be inferred under uncertainty or can be accessed directly from online information sources. One embodiment makes use of information from an online calendar to control the criteria used to make decisions about relaying information to a mobile device.

The invention discloses a method for detecting fire disaster smog by video on the basis of color saturation and motion mode, which comprises the following steps: firstly, extracting a foreground motion block from a video image acquired by a monitoring camera through a difference method; secondly, detecting the color saturation of the foreground motion block, estimating the direction of the motion block, and calculating the cumulant and the main moving direction of the motion block; and thirdly, calculating the color saturation detection percentage of each region, the average cumulant and the main moving direction ratio to form a characteristic vector, and adopting a Bayesian classifier to judge whether the smog is the fire disaster smog. The detection of the color saturation reflects a rough color distribution of the smog and eliminates a large amount of foreground interfering objects with glowing colors. The color detection in a blocking mode further improves the anti-interference performance of a system. The cumulant shows the characteristic of continuous movement nearby a smoldering point of the smog, and has very good anti-interference performance for a non-reciprocating object. The comprehensive utilization of the color saturation detection percentage, the average cumulant and the main movement ratio can greatly reduce the rate of false alarm for the system.

A system and method facilitating lithography defect solution generation is provided. The invention includes a defect solution component and a defect alert component. The defect solution component provides potential solution(s) to a defect within the lithography process utilizing artificial intelligence technique(s) (e.g., Bayesian learning methods that perform analysis over alternative dependent structures and apply a score, Bayesian classifiers and other statistical classifiers, including decision tree learning methods, support vector machines, linear and non-linear regression and/or neural network).

A device may facilitate a query dialog involving queries that successively modify a query state. However, fulfilling such queries in the context of possible query domains, query intents, and contextual meanings of query terms may be difficult. Presented herein are techniques for modifying a query state in view of a query by utilizing a set of query state modifications, each representing a modification of the query state possibly intended by the user while formulating the query (e.g., adding, substituting, or removing query terms; changing the query domain or query intent; and navigating within a hierarchy of saved query states). Upon receiving a query, an embodiment may calculate the probability of the query connoting each query state modification (e.g., using a Bayesian classifier), and parsing the query according to a query state modification having a high probability (e.g., mapping respective query terms to query slots within the current query intent).

According to an aspect of the invention, a method for training a classifier for classifying candidate regions in computer aided diagnosis of digital medical images includes providing a training set of annotated images, each image including one or more candidate regions that have been identified as suspicious, deriving a set of descriptive feature vectors, where each candidate region is associated with a feature vector. A subset of the features are conditionally dependent, and the remaining features are conditionally independent. The conditionally independent features are used to train a naïve Bayes classifier that classifies the candidate regions as lesion or non-lesion. A joint probability distribution that models the conditionally dependent features, and a prior-odds probability ratio of a candidate region being associated with a lesion are determined from the training images. A new classifier is formed from the naïve Bayes classifier, the joint probability distribution, and the prior-odds probability ratio.

The present application relates to an intelligent outpatient guiding method and device, a computer device and a storage medium. The method includes: acquiring patient inquiry data; extracting inquiryelement features from the patient inquiry data; converting the inquiry element features into feature vectors; inputting the feature vectors into a preset neural network classifier to obtain a first department matching probability; inputting the feature vectors into a preset Bayesian classifier to obtain a second department matching probability; and generating a department recommendation result isaccording to the first department matching probability and the second department matching probability. The method can improve the accuracy of patient registration.

The invention discloses an anomaly detection method based on network flow analysis. A complete network flow initial feature set is provided by deeply analyzing an IP (internet protocol) data packet, and the performance of an anomaly detection system can be fundamentally improved. A feature subset for anomaly detection is dynamically selected according to different types of network anomalies, finally, the class of an unknown sample is predicted according to the feature subset by a Bayes classifier, and the anomalies are prompted if the unknown sample is abnormal according to a prediction result. A data preprocessing module is used for processing preliminary data; a feature selecting module is used for selecting the proper feature subset for anomaly detection according to the types of the anomalies; and an anomaly detection module is used for prompting the anomalies after the anomalies are found. By the aid of dynamic feature selection algorithm, the optimal feature subset for detecting the anomalies can be dynamically selected according to the different types of anomalies, flow feature dimensions for detecting the anomalies can be reduced, and anomaly detection accuracy is improved.

The embodiment of the invention discloses a text sentiment classification method, which comprises the steps of finding a sentiment word in a text to be classified by referring to a preset sentiment word list, and acquiring a sentiment polarity corresponding to the sentiment word according to the sentiment word; utilizing two polarity conversion rules to judge whether the sentiment word has polarity conversion or not, and calculating probability of each word in the text to be classified appearing in the text of each polarity according to the sentiment polarity of the sentiment word and the polarity conversion result of the sentiment word; and utilizing a Bayes classifier model to classify the text to be classified according to the probability of each word appearing in the text of each polarity. According to the classification method, the classification effect is far higher than that of a traditional text sentiment classification method, the unfavorable influence of the sentiment word having the sentiment polarity conversion on the classification effect of the text can be avoided, and the classification accuracy of the text sentiment can be improved.

The invention provides a method for detecting an image spam email by local invariant features of pictures, which can extract the invariant region feature of junk information in the pictures by using a scale-invariant feature conversion algorithm and extract characters embedded into the pictures to classify the pictures so as to form a feature vector library of the pictures combining two features together. Experiments prove that the recall rate of the spam email can be improved and the program operation time and space can be saved. The method can extract the invariant region feature in the pictures to generate the feature vectors of the pictures, and a support vector machine classifier is used for training and testing. In the method, by utilizing the text messages embedded into the pictures, the text string in the pictures can be excavated by using a graphic character recognition technology and the string can be taken as the feature of the pictures, and the Bayesian classifier is used for training and testing. The feature vector of each picture is composed of the local invariant feature of the picture and the text string; and two types of classifiers are used for classifying by a stacking method to achieve the purpose of detecting the image spam email.

A road snow and rain state automatic identification method based on feature information classification comprises a step A of extracting a sample describing feature and constructing a Bayes classifier, and a step B of detecting the road state. The step A comprises a small step A 1 of acquiring a sample image, a small step A 2 of preprocessing the image, a small step A 3 of extracting a sampling image texture attribute value and an average gray value of a road effective coverage sample, a small step A 4 of calculating a probability density function of the road sample, and a small step A 5 of determining an operational rule of a type conditional probability density function to construct the Bayes classifier. The step B comprises a small step B 1 of acquiring a road detecting image, a small step B 2 of preprocessing the image, a small step B 3 of extracting a texture attribute value and an average gray value of a road detecting standard image frame, and a small step B 4 of judging the road surface state of the road. The road snow and rain state automatic identification method based on the feature information classification can adapt to various weather variations and complexity and changes of traffic road states, detection efficiency and accuracy are high, and cost is low, so that the road snow and rain state automatic identification method based on the feature information classification provides reference for traffic safety guarantee and traffic management.

The invention discloses a big data text classifying method based on cloud computing. The method comprises the following steps: respectively pre-processing training texts with class labels and without class labels to obtain corresponding training data sets; respectively carrying out feature selection on the training data sets to obtain corresponding dimensionally reduced training data sets; respectively calculating the dimensionally reduced training data sets according to a TFIDF weighted model, and respectively converting the training data sets to corresponding one-dimensional vectors; calculating the one-dimensional vectors with class labels according to a bayesian algorithm to obtain the prior probability of each class and the prior probability that each entry belongs to each class, and initializing the parameters of a bayesian classifier; utilizing an EM algorithm to optimize the parameters of the bayesian classifier so as to obtain a classifying model; carrying out text classification on the to-be-classified texts through the classifying model. Through combining a traditional naive bayesian classifying technology and Hadoop and EM algorithms, calculating speed limitation and training data limitation problems in actual application are improved, and the efficiency and the accuracy of the classifier are improved.

The invention discloses a method for performing emotional tendency classification to microblog by using emoticons. The method comprises the following steps: building a neutral emotion set, a passive emotion set and a positive emotion set; building a neutral emotion Bayes classifier by using the neutral emotion set, the passive emotion set and the positive emotion set; building a polar emotion Bayes emotion classifier by using the passive emotion set and the positive emotion set; performing emotion classification to the microblog by using the neutral emotion Bayes classifier and the polar emotion Bayes classifier. According to the method for performing emotional tendency classification to microblog by using emoticons, two sections of classification is built, namely, building the neutral emotion classifier to remove the microblog with neutral emotion, and building the polar emotion classifier to divide the microblog with the polar emotion into the passive emotion and the positive emotion; the classifier is rapid in classifying speed, small in occupied space and robust; attitude of people to current hot topics or events and emotion of net citizens can be accurately understood through the microblog, so that the method for performing emotional tendency classification to microblog by using emoticons has important help for social scientific research and survey.

The invention discloses a POI (point of interest) Chinese text categorizing method based on a local random word density model. The method includes: judging whether the subject of a text is related to POI or not, and using a modified word concentration, dispersion and frequency method to screen out feature words to build a feature space; performing local area dividing according to the similarity degree of the text and POI category, converting the text into feature vectors in each local area through feature mapping matrixes, and using SVM to perform POI text categorization. The method is good in execution efficiency and categorizing coverage and accuracy. The method is planned to be combined with large knowledge libraries of Hownet so as to capture semantic concepts of low-frequency words and unseen words and further increase the recognition capability of POI text differences, and the problem that good categorization performance cannot be obtained by existing conventional feature evaluation functions and text dimensionality reduction methods is solved.

The invention relates to a text classification method and device based on Hadoop. The text classification method based on the Hadoop comprises the steps that text which is processed in a word segmentation mode is stored in a training data set; the text of different classes is equal in quantity; text files of the training data set are written in a SequenceFile; statistics is carried out on the number of entries contained in the text and the classes of the entries through a MapReduce module, a TF-IDF value of each entry of the text is calculated according to a TF-IDF weighting module, the text is converted into one-dimensional vectors for a Bayes classifier according to the TF-IDF values, statistics is carried out on the lengths of the text of the text files, and weighting is carried out on the one-dimensional vectors according to the lengths; a classification model is obtained; the text to be classified is classified through the classification model. The text classification method based on the Hadoop can solve the problem that data obtained through a traditional training classifier are unbalanced.