1573 results about "Classifier (UML)" patented technology

A classification system includes memory which stores, for each of a set of classes, a classifier model for assigning a class probability to a test sample from a target domain. The classifier model has been learned with training samples from the target domain and from at least one source domain. Each classifier model models the respective class as a mixture of components, the component mixture including a component for each source domain and a component for the target domain. Each component is a function of a distance between the test sample and a domain-specific class representation which is derived from the training samples of the respective domain that are labeled with the class, each of the components in the mixture being weighted by a respective mixture weight. Instructions, implemented by a processor, are provided for labeling the test sample based on the class probabilities assigned by the classifier models.

A module information extraction system capable of extracting information from natural language documents. The system includes a plurality of interchangeable modules including a data preparation module for preparing a first set of raw data having class labels to be tested, the data preparation module being selected from a first type of the interchangeable modules. The system further includes a feature extraction module for extracting features from the raw data received from the data preparation module and storing the features in a vector format, the feature extraction module being selected from a second type of the interchangeable modules. A core classification module is also provided for applying a learning algorithm to the stored vector format and producing therefrom a resulting classifier, the core classification module being selected from a third type of the interchangeable modules. A testing module compares the resulting classifier to a set of preassigned classes, where the testing module is selected from a fourth type of the interchangeable modules, where the testing module tests a second set of raw data having class labels received by the data preparation module to determine the degree to which the class labels of the second set of raw data approximately corresponds to the resulting classifier.

A two-class analysis system for summarizing features and determining features appropriate to use in training a classifier related to a data mining operation. Exemplary embodiments describe how to select features which will be suited to training a classifier used for a two-class text classification problem. Bi-Normal Separation methods are defined wherein there is a measure of inverse cumulative distribution function of a standard probability distribution and representative of a difference between occurrences of the feature between said each class. In addition to training a classifier, the system provides a means of summarizing differences between classes.

The invention discloses an Android malicious application detection method and system based on multi-feature fusion. The method comprises the following steps that: carrying out decompilation on an Android application sample to obtain a decompilation file; extracting static features from the decompilation file; operating the Android application sample in an Android simulator to extract dynamic features; carrying out feature mapping on the static features and the dynamic features by the text Hash mapping part of a locality sensitive Hash algorithm, mapping to a low-dimensional feature space to obtain a fused feature vector; and on the basis of the fused feature vector, utilizing a machine learning classification algorithm to train to obtain a classifier, and utilizing the classifier to carry out classification detection. By use of the method, the high-dimensional feature analysis problem of a malicious code rare sample family can be solved, and detection accuracy is improved.

An instance-level retrieval method and system are provided. A representation of a query image is embedded in a multi-dimensional space using a learned projection. The projection is learned using category-labeled training data to optimize a classification rate on the training data. The joint learning of the projection and the classifiers improves the computation of similarity/distance between images by embedding them in a subspace where the similarity computation outputs more accurate results. An input query image can thus be used to retrieve similar instances in a database by computing the comparison measure in the embedding space.

A classifier training method and apparatus for training, a linear classifier trained by the method, and its use, are disclosed. In training the linear classifier, signatures for a set of training samples, such as images, in the form of multi-dimension vectors in a first multi-dimensional space, are converted to a second multi-dimension space, of the same or higher dimensionality than the first multi-dimension space, by applying a set of embedding functions, one for each dimension of the vector space. A linear classifier is trained in the second multi-dimension space. The linear classifier can approximate the accuracy of a non-linear classifier in the original space when predicting labels for new samples, but with lower computation cost in the learning phase.

The present invention relates generally to the classification of items into categories, and more generally, to the automatic selection of different classifiers at different places within a hierarchy of categories. An exemplary hierarchical categorization method uses a hybrid of different classification technologies, with training-data based machine-learning classifiers preferably being used in those portions of the hierarchy above a dynamically defined boundary in which adequate training data is available, and with a-priori classification rules not requiring any such training-data being used below that boundary, thereby providing a novel hybrid categorization technology that is capable of leveraging the strengths of its components. In particular, it enables the use of human-authored rules in those finely divided portions towards the bottom of the hierarchy involving relatively close decisions for which it is not practical to create in advance sufficient training data to ensure accurate classification by known machine-learning algorithms, while still facilitating eventual change-over within the hierarchy to machine learning algorithms as sufficient training data becomes available to ensure acceptable performance in a particular sub-portion of the hierarchy.

The invention discloses a character detection method and device based on deep learning. The method comprises the steps: designing a multilayer convolution neural network structure, and enabling each character to serve as a class, thereby forming a multi-class classification problem; employing a counter propagation algorithm for the training of a convolution neural network, so as to recognize a single character; minimizing a target function of the network in a supervision manner, and obtaining a character recognition model; finally employing a front-end feature extracting layer for weight initialization, changing the node number of a last full-connection layer into two, enabling a network to become a two-class classification model, and employing character and non-character samples for training the network. Through the above steps, one character detection classifier can complete all operation. During testing, the full-connection layer is converted into a convolution layer. A given input image needs to be scanned through a multi-dimension sliding window, and a character probability graph is obtained. A final character region is obtained through non-maximum-value inhibition.

A fully or semi-automated, integrated learning, labeling and classification system and method have closed, self-sustaining pattern recognition, labeling and classification operation, wherein unclassified data sets are selected and converted to an assembly of graphic and text data forming compound data sets that are to be classified. By means of feature vectors, which can be automatically generated, a machine learning classifier is trained for improving the classification operation of the automated system during training as a measure of the classification performance if the automated labeling and classification system is applied to unlabeled and unclassified data sets, and wherein unclassified data sets are classified automatically by applying the machine learning classifier of the system to the compound data set of the unclassified data sets.

The invention belongs to the data mining and natural language processing technical field, specifically a product comment analyzing method and system with learning supervising function. According to the method, multiple categories (product features) are artificially defined aiming at a specific product; firstly, product feature aspect classification is carried out to collected user comments by a machine learning and training classifier successively; then emotion analysis is carried out to the comment texts classified by a training classifier; and finally quantitative comments on each feature of the product are summarized for a user through comprehensively counting the product features related to a great deal of comment texts and corresponding emotional tendencies. The method and the system of the invention are relatively efficient, rapid, simple and convenient; the classified contents are all user concerned contents; the analysis result is provided to the user visually and clearly; and the work of viewing a great deal of comments can be removed.

Automatic relationship extraction is provided. A machine learning approach using statistical entity-type prediction and relationship predication models built from large unlabeled datasets is interactively combined with minimal human intervention and a light pattern-based approach to extract relationships from unstructured, semi-structured, and structured documents. Training data is collected from a collection of unlabeled documents by matching ground truths for a known entity from existing fact databases with text in the documents describing the known entity and corresponding models are built for one or more relationship types. For a modeled relationship-type, text chunks of interest are found in a document. A machine learning classifier predicts the probability that one of the text chunks is the entity being sought. The combined machine learning and light pattern-based approach provides both improved recall and high precision through filtering and allows constraining and normalization of the extracted relationships.

The invention discloses a self-adaptive cascade classifier training method based on online learning, which comprises the following steps: (1), preparing a training sample set with a small quantity of samples, and training an initial cascade classifier HC(x) in a cascade classifier algorithm; (2), using the HC(x) for traversal of image frames to be detected, extracting areas with sizes identical to the sizes of the training samples one by one, calculating a feature value set, classifying the areas with the initial cascade classifier, and judging whether the areas are target areas, thereby completing target detection; (3) tracking the detected targets in a particle filtering algorithm, verifying the target detection results through tracking, marking detection with errors as a negative sample for online learning, obtaining different attitudes of real targets through tracking and extracting a positive sample for online learning; and (4) carrying out online training and updating for the initial cascade classifier HC(x) in a self-adaptive cascade classifier algorithm when an online learning sample is obtained, thereby gradually improving the target detection accuracy of the classifier.

A class is to be characterized of a probabilistic classifier or clustering system that includes probabilistic model parameters. For each of a plurality of candidate words or word combinations, divergence of the class from other classes is computed based on one or more probabilistic model parameters profiling the candidate word or word combination. One or more words or word combinations are selected for characterizing the class as those candidate words or word combinations for which the class has substantial computed divergence from the other classes.

An “active learning” method trains a compact classifier for view-based object recognition. The method actively generates its own training data. Specifically, the generation of synthetic training images is controlled within an iterative training process. Valuable and/or informative object views are found in a low-dimensional rendering space and then added iteratively to the training set. In each iteration, new views are generated. A sparse training set is iteratively generated by searching for local minima of a classifier's output in a low-dimensional space of rendering parameters. An initial training set is generated. The classifier is trained using the training set. Local minima are found of the classifier's output in the low-dimensional rendering space. Images are rendered at the local minima. The newly-rendered images are added to the training set. The procedure is repeated so that the classifier is retrained using the modified training set.

The present invention provides a technique for automated selection of a parameterized operator sequence to achieve a pattern classification task. A collection of labeled data patterns is input and statistical descriptions of the inputted labeled data patterns are then derived. Classifier performance for each of a plurality of candidate operator/parameter sequences is determined. The optimal classifier performance among the candidate classifier performances is then identified. Performance metric information, including, for example, the selected operator sequence/parameter combination, will be outputted. The operator sequences selected can be chosen from a default set of operators, or may be a user-defined set. The operator sequences may include any morphological operators, such as, erosion, dilation, closing, opening, close-open, and open-close.

Multi-label classification is performed by (i) applying a set of trained base classifiers to an object to generate base classifier label prediction sets comprising subsets of a set of labels; (ii) constructing a set of second level features including at least one second level feature defined by a predetermined combination of two or more of the base classifier label prediction sets; and (iii) applying a second level classifier to label the object with a set of one or more labels comprising a subset of the set of labels, labeling being based on the set of second level features. The multi-label classifier is trained by: (iv) applying operations (i) and (ii) to labeled training objects of a set of labeled training objects to generate training metadata comprising sets of second level features for the labeled training objects; and (v) training the second level classifier using the training metadata.

The invention provides a training method and a device for a sensitive picture classifier; the regional shape characteristic of the training picture set is extracted; the distribution characteristics of the regional shape characteristic in the positive example sample set, the first negative example sample set and the second negative example sample set are measured; the divisibility of the regional shape characteristic is determined according to the distribution characteristics; the regional shape characteristic with the divisibility relative to the first negative example sample set is marked as the first characteristic group; the regional shape characteristic with the divisibility relative to the second negative example sample set is marked as the second characteristic group; the first classifier is obtained through the characteristic training of the first characteristic group; the second classifier is obtained through the characteristic training of the second characteristic group; the invention also provides the method and the device which adopt the sensitive picture classifier to recognize the sensitive pictures; the invention improves the accuracy of the sensitive picture recognition.

A software defect priority prediction method based on an improved support vector machine is mainly characterized in that an improved support vector machine model is used for modeling defect priority prediction and judging and predicting defect report processing priority. The software defect priority prediction method includes the steps: firstly, selecting solved, closed and determined error report as training data; secondly, extracting needed characteristics; thirdly, giving a sampling weight to each sample and training a classifier to classify the samples by the aid of the support vector machine on the samples; fourthly, redistributing weight vectors by the aid of obtained error rate in the manner of distributing larger weights to mistakenly classified samples and distributing smaller weights to correctly classified samples; and fifthly, sequentially iterating in the manner to finally obtain a strong classifier equal to the weighted sum of a plurality of weak classifiers. The classifiers are trained by means of machine learning, so that defect priority is automatically determined, and consumption of staff and cost is reduced.

The invention discloses a composite fault diagnosis method and device based on a multi-label classification convolutional neural network. The method comprises: steps 1, collecting and extracting vibration acceleration signal samples under single fault and composite fault working conditions; step 2, giving a label to each sample according to the type, and dividing the sample into a training set anda test set; step 3, establishing a deep one-dimensional convolutional neural network, and setting a Sigmoid activation function and a boundary loss function Margin Loss; step 4, directly inputting the vibration data of the training set into the built deep one-dimensional convolutional neural network for training; and step 5, selecting an optimal model through Grid Search, and applying the optimalmodel to the test set to obtain a fault state classification result. The method enables the classifier to adaptively output a plurality of labels for the composite fault, is high in fault diagnosis precision, can overcome the limitation that a traditional classifier can only output one label, and achieves the diagnosis of the composite fault.

Systems, methods, and computer program products implementing techniques for training classifiers. The techniques include receiving a training set that includes positive images and negative images, receiving a restricted set of linear operators, and using a boosting process to train a classifier to discriminate between the positive and negative images. The boosting process is an iterative process. The iterations include a first iteration where a classifier is trained by (1) testing some, but not all linear operators in the restricted set against a weighted version of the training set, (2) selecting for use by the classifier the linear operator with the lowest error rate, and (3) generating a re-weighted version of the training set. The iterations also include subsequent iterations during which another classifier is trained by repeating steps (1), (2), and (3), but using in step (1) the re-weighted version of the training set generated during a previous iteration.

The invention discloses a multi-label classifier constructing method based on cost-sensitive active learning. Specific label information of each sample is directly used, an initial classifier is trained on a marked set, then, learning is performed on a non-marked set through a current classifier, a certain number of sample-label pairs of non-marked samples largest in misclassification cost are selected, real label values of the selected sample-label pairs are marked and added into the marked set, and a training sample set and the classifiers are updated. According to the method, target misclassification cost can be achieved with a small number of iteration times, and learning efficiency is greatly improved; sampling granularity is shrunk to the sample-label pairs, cost for marking samples is greatly reduced, and more remarkable effect is achieved in multi-label classification with a large label number.

The invention discloses a method for early classifying imbalance multi-variable time sequence data. The method comprises the following steps: carrying out the under-sampling for a large category of category data sets according to an imbalance ratio for the inter-category data scale imbalance problem into a plurality of subsets, and combining the subsets with a small category of category data to form a plurality of training subsets; extracting and selecting core features of each training subset, and establishing a sub-classifier based on a rule according to the core features, wherein the feature selection process is realized in a clustering way in order to solve the imbalance problem of the data scale of the intra-category sub-concepts so as to guarantee the diversity of the core features; and solving the weight of a classifying effect of data in the training set by utilizing the sub-classifier on the basis of each sub-classifier, and establishing an integrated classifier. The classifier can solve the multi-variable time sequence classifying problem of the imbalance data set, the accuracy is relatively high, and the earliness degree is good.