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2140 results about "Support vector machine" patented technology

In machine learning, support-vector machines (SVMs, also support-vector networks) are supervised learning models with associated learning algorithms that analyze data used for classification and regression analysis. Given a set of training examples, each marked as belonging to one or the other of two categories, an SVM training algorithm builds a model that assigns new examples to one category or the other, making it a non-probabilistic binary linear classifier (although methods such as Platt scaling exist to use SVM in a probabilistic classification setting). An SVM model is a representation of the examples as points in space, mapped so that the examples of the separate categories are divided by a clear gap that is as wide as possible. New examples are then mapped into that same space and predicted to belong to a category based on the side of the gap on which they fall.

Object detector, object detecting method and robot

An object detector, an object detecting method and a robot can reduce diction errors of detecting wrong objects without increasing the volume of the computational operation to be performed to detect the right object. A face detector 101 comprises a face detecting section 110 that operates like a conventional face detecting section and is adapted to roughly select face candidates from an input image by template matching and detect face candidates by means of a support vector machine for face recognition, a non-face judging section 120 that detects non-face candidates that are judged to be non-faces and removes them from the face candidates selected by the face detecting section 110 and a skin tracker section 114 for tracking a face region after the non-face judgment. When the assumed distance between the face detector and the face as computed from the input image and the measured distance as measured by a distance sensor show a large difference, when the color variance of the face candidate is small, when the occupancy ratio of the skin color region is large and when the change in the size of the face region is large after the elapse of a predetermined time, the non-face judging section 120 judges such face candidates as non-faces and removes them from the face candidates.

Imaging based symptomatic classification and cardiovascular stroke risk score estimation

Characterization of carotid atherosclerosis and classification of plaque into symptomatic or asymptomatic along with the risk score estimation are key steps necessary for allowing the vascular surgeons to decide if the patient has to definitely undergo risky treatment procedures that are needed to unblock the stenosis. This application describes a statistical (a) Computer Aided Diagnostic (CAD) technique for symptomatic versus asymptomatic plaque automated classification of carotid ultrasound images and (b) presents a cardiovascular stroke risk score computation. We demonstrate this for longitudinal Ultrasound, CT, MR modalities and extendable to 3D carotid Ultrasound. The on-line system consists of Atherosclerotic Wall Region estimation using AtheroEdge™ for longitudinal Ultrasound or Athero-CTView™ for CT or Athero-MRView from MR. This greyscale Wall Region is then fed to a feature extraction processor which computes: (a) Higher Order Spectra; (b) Discrete Wavelet Transform (DWT); (c) Texture and (d) Wall Variability. The output of the Feature Processor is fed to the Classifier which is trained off-line from the Database of similar Atherosclerotic Wall Region images. The off-line Classifier is trained from the significant features from (a) Higher Order Spectra; (b) Discrete Wavelet Transform (DWT); (c) Texture and (d) Wall Variability, selected using t-test. Symptomatic ground truth information about the training patients is drawn from cross modality imaging such as CT or MR or 3D ultrasound in the form of 0 or 1. Support Vector Machine (SVM) supervised classifier of varying kernel functions is used off-line for training. The Atheromatic™ system is also demonstrated for Radial Basis Probabilistic Neural Network (RBPNN), or Nearest Neighbor (KNN) classifier or Decision Trees (DT) Classifier for symptomatic versus asymptomatic plaque automated classification. The obtained training parameters are then used to evaluate the test set. The system also yields the cardiovascular stroke risk score value on the basis of the four set of wall features.

Method for establishing virtual reality excavation dynamic smart load prediction models

The invention discloses a method for establishing virtual reality excavation dynamic smart load prediction models. The method includes the steps that the knowledge excavation technology is adopted so that a virtual reality analysis environment can be formed, the influence relation between fixed quantities is explored, and an input variable candidate set is determined; smart load prediction models of a support vector machine of a self-adaptive structure and an Elman neural network and the like are established, wherein input variables are determined by the support vector machine through the attribute screening technology and parameters are optimized by the support vector machine through a flora tendency differential evolutionary algorithm; a region load smart load prediction model based on data slice excavation is established; a load curve prediction model combined with dynamic electrovalence factors, user characteristics and the user response electric quantity is established, so that linked correcting prediction of loads, electrovalence and the response electric quantity is achieved. According to the method, the prediction models suitable for the actual condition of a smart power grid of China are established, the scale of construction of renewable energy sources is reasonably planned, more efficient power utilization of users is facilitated, and reasonable arrangement of power supply resources of power enterprises is facilitated.

Magnetic tile surface defect feature extraction and defect classification method based on machine vision

The invention provides a magnetic tile surface defect feature extraction and defect classification method based on machine vision. A concrete algorithm comprises a first step of building a 5-scale and 8-direction Gabor filter bank suitable for magnetic tile surface defect feature extraction, conducting filtering to an original image and obtaining a 40-width component plot, a second step of respectively extracting a gray average and a variance feature of the component plot and forming a 80-dimension feature vector, a third step of conducting dimensionality reduction to the original 80-dimension feature vector through a principal component analysis (PCA) method and an independent component analysis (ICA) method, removing relevance and redundancy and obtaining a 20-dimension feature vector, a fourth step of conducting normalization pretreatment to feature vector data, wherein the original data are normalized between zero and one, and a fifth step of adopting a grid method and a K-CV method to achieve SVM parameter optimization at first and training an SVM model using training sample data offline, wherein pretreated testing sample data are input into a support vector machine during online testing, and automatic classification and identification of defects can be achieved. The feature extraction method can effectively filter interference and prominent defects of magnetic tile surface texture, extracted features can reflect defect information accurately, data values are small, and a classifier used for classifying the defects can achieve defect identification fast and accurately online.

Text sentiment analysis method and device, storage medium and computer equipment

The invention relates to a text sentiment analysis method and device, a storage medium and computer equipment. A sentence vector in a sentence in a test text is obtained and is formed in a way that the word vectors of words in the sentence are connected, and the sentence vector is independently input into two preset convolutional neural networks and one two-way long short-term memory neural network model to be preprocessed to obtain three sentence feature vectors of the sentence. Three sentence feature vectors are connected, the connected sentence feature vectors are classified through a classifier SVM (Support Vector Machine) to obtain the sentiment classification result of the sentence, and the emotional tendency of a test text can be obtained according to the sentiment classification result of the sentence. By use of the method, the convolutional neural network can be combined to effectively extract local features, the two-way long short-term memory neural network can effectively analyze the advantages of time sequence features, the test text is subjected to the sentiment analysis through the method to obtain robustness with higher emotional tendency and generalization ability,and efficiency is higher.

Device and method for automatically monitoring telephone call behavior of driver when driving

The invention relates to a device and a method for automatically monitoring a telephone call behavior of a driver when driving and belongs to the fields of intelligent traffic and aided driving. The device comprises an image acquisition device, a calculating device, a warning device and a wireless transmission device. The method comprises the following steps of firstly acquiring a head and nearby area image of the driver, secondly, obtaining the positions of the face and both hands of the driver in the image by skin color detection, and classifying by a support vector machine to determine whether the driver makes a telephone call or not; warning the driver if the driver makes the telephone call; uploading the image that the driver makes the telephone call to the monitoring center of the road transport bureau through a wireless network if the warning is useless to be used as an evidence that the driver breaks the law. According to the device and the method for automatically monitoring the telephone call behavior of the driver when driving disclosed by the invention, the unlawful act of the driver can be effectively monitored, the working strength of law enforcement personnel is relieved, the working efficiency is improved, and the occurrence rate of traffic accidents is reduced.

Methods and systems for identification of DNA patterns through spectral analysis

Spectrogram extraction from DNA sequence has been known since 2001. A DNA spectrogram is generated by applying Fourier transform to convert a symbolic DNA sequence consisting of letters A, T, C, G into a visual representation that highlights periodicities of co-occurrence of DNA patterns. Given a DNA sequence or whole genomes, with this method it is easy to generate a large number of spectrogram images. However, the difficult part is to elucidate where are the repetitive patterns and to associate a biological and clinical meaning to them. The present disclosure provides systems and methods that facilitate the location and/or identification of repetitive DNA patterns, such as CpG islands, Alu repeats, tandem repeats and various types of satellite repeats. These repetitive elements can be found within a chromosome, within a genome or across genomes of various species. The disclosed systems and methods apply image processing operators to find prominent features in the vertical and horizontal direction of the DNA spectrograms. Systems and methods for fast, full scale analysis of the derived images using supervised machine learning methods are also disclosed. The disclosed systems and methods for detecting and/or classifying repetitive DNA patterns include: (a) comparative histogram method, (b) feature selection and classification using support vector machines and genetic algorithms, and (c) generation of spectrovideo from a plurality of spectral images.

Method for identifying local discharge signals of switchboard based on support vector machine model

The invention discloses a method for identifying local discharge signals of a switchboard based on a support vector machine model. The method comprises a model training process and an audio identifying process, and particularly comprises the following steps of: preprocessing audio signals; extracting effective audios according to short-time energy and a zero-crossing rate; segmenting the effective audios and extracting characteristic parameters such as Mel cepstrum coefficients, first order difference Mel cepstrum coefficients, high zero-crossing rate and the like of each segment of the audios; training a sample set by using a support vector machine tool, and establishing a corresponding support vector machine model; after preprocessing audio signals to be identified and extracting and segmenting the effective audios, classifying and identifying segment-characteristic-based samples to be tested according to the support vector machine model; and post-processing classification results, and judging whether partial discharge signals exist. By using the method, the existence of the partial discharge signals of the switchboard is accurately identified, the happening of major accidents involving electricity is prevented and avoided, economic losses caused by insulation accidents are reduced, and the power distribution reliability is improved.

Method for three-dimensional reconstruction of laser speckle structured light and depth information

The invention provides a method for three-dimensional reconstruction of laser speckle structured light and depth information. The three-dimensional reconstruction technology is an important subject for machine vision research and refers to the content that a three-dimensional space geometrical shape of a three-dimensional body is restored through images of the three-dimensional body. Generally, three-dimensional reconstruction is conducted through the binocular parallax principle of a binocular camera or through a triangulation method or space codes are obtained through the structured light and the depth information is obtained through the triangulation method. The method aims at obtaining the depth information through the laser speckle structured light, a similar invention such as the kinect of the Microsoft Corporation also obtains the depth information (namely different depths are matched through a cross-correlation function of laser speckles) of an object through the method, and the difference is an algorithm for obtaining the depth information through the speckles. According to the method, parallel code number sorting is conducted on each pixel block one by one by a thinning window through multiple support vector machines, so that the depth of each pixel window is obtained, coordinates under a world coordinate system of the object are obtained by inversely solving a camera model through the depth information, and therefore the depth information with the higher accuracy can be obtained.
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