752 results about "Online machine learning" patented technology

The present invention provides a system and method for predicting and preventing unauthorized intrusion in a computer configuration. Preferably, the invention comprises a communication network to which at least two computing devices connect, wherein at least one of the computing devices is operable to receive data transmitted by the other computing device. The invention further comprises a database that is accessible over the network and operable to store information related to the network. A vulnerability assessment component is provided that is operable to execute a command over the communication network, and a data monitoring utility operates to monitor data transmitted over the communication network as the vulnerability assessment component executes commands. Also, an intrusion detection component is included that is operable to provide a simulated copy of the network, to generate a first data transmission on the simulated copy of the network that represents a second data transmission on the communication network, and to compare the first data transmission with a second data transmission. The vulnerability assessment component preferably interfaces with the intrusion detection component to define rules associated with the first and second data transmissions, to store the rules in the database, and to retrieve the rules from the database in order to predict and prevent unauthorized intrusion in the computer configuration.

Method for detecting malicious behavioral patterns which are related to malicious software such as a computer worm in computerized systems that include data exchange channels with other systems over a data network. Accordingly, hardware and/or software parameters are determined in the computerized system that is can characterize known behavioral patterns thereof. Known malicious code samples are learned by a machine learning process, such as decision trees and artificial neural networks, and the results of the machine learning process are analyzed in respect to the behavioral patterns of the computerized system. Then known and unknown malicious code samples are identified according to the results of the machine learning process.

A computing device receives a training data set that includes a plurality of positive examples of sensitive data and a plurality of negative examples of sensitive data via a user interface. The computing device analyzes the training data set using machine learning to generate a machine learning-based detection (MLD) profile that can be used to classify new data as sensitive data or as non-sensitive data. The computing device displays a quality metric for the MLD profile in the user interface.

According to various embodiments of the present invention, an automated technique is implemented for resolving and merging fields accurately and reliably, given a set of duplicated records that represents a same entity. In at least one embodiment, a system is implemented that uses a machine learning (ML) method, to train a model from training data, and to learn from users how to efficiently resolve and merge fields. In at least one embodiment, the method of the present invention builds feature vectors as input for its ML method. In at least one embodiment, the system and method of the present invention apply Hierarchical Based Sequencing (HBS) and/or Multiple Output Relaxation (MOR) models in resolving and merging fields. Training data for the ML method can come from any suitable source or combination of sources.

Tomographic/tomosynthetic image reconstruction systems and methods in the framework of machine learning, such as deep learning, are provided. A machine learning algorithm can be used to obtain an improved tomographic image from raw data, processed data, or a preliminarily reconstructed intermediate image for biomedical imaging or any other imaging purpose. In certain cases, a single, conventional, non-deep-learning algorithm can be used on raw imaging data to obtain an initial image, and then a deep learning algorithm can be used on the initial image to obtain a final reconstructed image. All machine learning methods and systems for tomographic image reconstruction are covered, except for use of a single shallow network (three layers or less) for image reconstruction.

Techniques for information retrieval include receiving a plurality of data records, each data record including data fields associated with a business enterprise, the data fields including a name of the business enterprise; updating a plurality of database records associated with the received plurality of data records stored in a database, each database record including attributes including the name of the business enterprise and an alias associated with the name of the business enterprise; receiving a query for a particular database record, the query including at least one of the name of the business enterprise or the alias associated with the name of the business enterprise; and preparing for display, in response to the query, one or more of the database records based on at least one of the name of the business enterprise or the alias associated with the name of the business enterprise.

Determining semantically equivalent text or questions using hybrid representations based on neural network learning. Weighted bag-of-words and convolutional neural networks (CNN) based distributed vector representations of questions or text may be generated to compute the semantic similarity between questions or text. Weighted bag-of-words and CNN based distributed vector representations may be jointly used to compute the semantic similarity. A pair-wise ranking loss function trains neural network. In one embodiment, the parameters of the system are trained by minimizing a pair-wise ranking loss function over a training set using stochastic gradient descent (SGD).

The invention relates to a method for distinguishing false iris images based on robust texture features and machine learning, which comprises the following steps: preprocessing true iris images or false iris images; extracting the partitioned statistical features of a robust weighted partial binary pattern; and carrying out training and sorting of a support vector machine, and judging whether thetest images are false iris images or not according to the output result of a sorter. The method of the invention combines SIFT descriptors and partial binary pattern features to extract the robust texture features, the description of textures is more stable because of the robustness of the SIFT to brightness, translation, rotation and scale change, and the support vector machine enables the method to have better universality. The invention can be used for effectively distinguishing the false iris images, has the advantages of high precision, high robustness and high reliability, can be used for distinguishing false irises such as paper printing irises, color printing contact lenses, synthetic eyes and the like, and can improve the safety of the system when being applied to the applicationsystem in which iris recognition is used for carrying out identification.

Malware detection methods systems, and apparatus are described. Malware may be detected by obtaining a plurality of malware binary executables and a plurality of goodware binary executables, decompiling the plurality of malware binary executables and the plurality of goodware binary executable to extract corresponding assembly code for each of the plurality of malware binary executables and the plurality of goodware binary executable, constructing call graphs for each of the plurality of malware binary executables and the plurality of goodware binary executables from the corresponding assembly code, determining similarities between the call graphs using graph kernels applied to the call graphs for each of the plurality of malware binary executables and the plurality of goodware binary executables, building a malware detection model from the determined similarities between call graphs by applying a machine learning algorithm such as a deep neural network (DNN) algorithm to the determined similarities, and identifying whether a subject executable is malware by applying the built malware detection model to the subject executable.

Data center activity traces form a corpus used for machine learning. The data in the corpus are putatively normal but may be tainted with latent anomalies. There is a statistical likelihood that the corpus represents predominately legitimate activity, and this likelihood is exploited to allow for a targeted examination of only the data representing possible anomalous activity. The corpus is separated into clusters having members with like features. The clusters having the fewest members are identified, as these clusters represent potential anomalous activities. These clusters are evaluated to determine whether they represent actual anomalous activities. The data from the clusters representing actual anomalous activities are excluded from the corpus. As a result, the machine learning is more effective and the trained system provides better performance, since latent anomalies are not mistaken for normal activity.

The invention comprises a bimodal man-man conversation sentiment analysis system and a bimodal man-man conversation sentiment analysis method based on machine learning. The bimodal man-man conversation sentiment analysis system is characterized by comprising a speech recognition module, a text deep-layer feature extraction module, a speech segmentation module, an acoustic feature extraction module, a feature fusion module and an sentiment analysis module, wherein the speech recognition module is used for recognizing speech content and a time label; the text deep-layer feature extraction module is used for completing the extraction of text deep-layer word level features and text deep-layer sentence level features; the speech segmentation module is used for segmenting single sentence speech from entire speech; the acoustic feature extraction module is used for completing the extraction of acoustic features of the speech; the feature fusion module is used for fusing the obtained text deep-layer features with the acoustic features; and the sentiment analysis module is used for acquiring sentiment polarities of the speech to be subjected to sentiment analysis. The bimodal man-man conversation sentiment analysis method can integrate the text and audio modals for recognizing conversation sentiment, and fully utilizes features of word vectors and sentence vectors, thereby improving the precision of recognition.

The invention discloses a machine learning-based wireless sensing motion identification method which comprises the following steps: a step of data collection, a step of data denoising operation, a step of feature extraction and a step of SVM model training and identifying operation. During data collection operation, absolute value of a group of CSI data collected on each sampling point is obtained and is read into a 30*Nr*Nt matrix form. A PCA mode is mainly adopted for the data denoising operation. Feature extraction operation can be conducted based on discrete wavelet transformation. To make SVM model training convenient, training samples are subjected to Kmeans clustering operation via the machine learning-based wireless sensing motion identification method, n clustering centers can be used as word bags, and voting operation is performed based on feature vectors and best matching items of all the word bags; when the matrix form feature vectors are converted into column vectors, SVM model training can be realized conveniently. The machine learning-based wireless sensing motion identification method is a human body behavior identification method which is high in identification accuracy and high in robustness for environment change.

A system for using machine-learning to create a model for performing integrated circuit layout extraction is disclosed. The system of the present invention has two main phases: model creation and model application. The model creation phase comprises creating one or more extraction models using machine-learning techniques. First, a complex extraction problem is decomposed into smaller simpler extraction problems. Then, each smaller extraction problem is then analyzed to identify a set of physical parameters that fully define the smaller extraction problem. Next, models are created using machine learning techniques for all of the smaller simpler extraction problems. The machine learning is performed by first creating training data sets composed of the identified parameters from typical examples of the smaller extraction problem and the answers to those example extraction problems as solved using a highly accurate physics-based field solver. The training sets are then used to train the models. In one embodiment, neural networks are used to model the extraction problems. Bayesian inference is employed by one embodiment in order to train the neural network models. Bayesian inference may be implemented with normal Monte Carlo techniques or Hybrid Monte Carlo techniques. After the creation of a set of models for each of the smaller simpler extraction problems, the machine-learning based models may be used for extraction.

A specific clothing image identification and detection method based on machine learning is provided. The invention relates to an image identification and detection method. The purpose of the invention is to solve the problems of the prior art that a manual detection way cannot realize searching of images of specific kinds of clothing because of a large quantity. The method includes the following processes: firstly, identifying image contents, and obtaining identified image contents; constructing an image classification database, processing images in the image classification database through a self-service sampling method, and obtaining processed images; obtaining an optimized convolution neural network; carrying out model integration; obtaining N individual learning devices, and combining the N learning devices through a simple ballot way; and secondly, utilizing a Faster R-CNN method to detect the identified image contents obtained in the first step. The specific clothing image identification and detection method is applied to the clothing image identification and detection field.