49 results about "Multiclass classification" patented technology

The invention relates to a crop drought degree prediction method based on unmanned aerial vehicle remote sensing monitoring. The method comprises the following steps: S1, data collection: collecting a multispectral image and a thermal infrared image of a farmland in real time through a remote sensing sensor; s2, image preprocessing: carrying out preprocessing operation on the collected multispectral image and thermal infrared image; s3, class specific feature selection: dividing the farmland into different classes according to the types, growth stages and expected drought degree grades of the crops, decomposing a multi-class classification problem into a plurality of dichotomy problems, and constructing a deep learning model for feature learning and importance evaluation for each dichotomy problem to obtain a class specific feature selection result; a class specific feature set for each class is formed, and class specific features for different classes are fused to form a comprehensive feature set; s4, model construction and training: constructing a drought degree prediction model according to the comprehensive feature set; and S5, drought degree prediction: realizing real-time monitoring and prediction of drought according to the real-time data and the prediction model.

Systems and methods for automatically populating documents about special entities are disclosed herein. An example method is performed by one or more processors of a computing system. The example method may include receiving user data, extracting a list of entities associated with the user and a list of events that occurred between the user and the entities, transforming metadata for the events associated with entities of interest into vectorized embeddings, selectively classifying, using a binary classifier model, ones of the entities as special entities and ones of the events as special events for a set of documents, assigning, using a multi-class classifier model, one of a plurality of categories to each special event associated with each special entity, each of the categories mapping to a corresponding section within the set of documents, and populating, for each special entity, the corresponding sections within the set of documents based on the categories.

In some embodiments, a method for classifying elements of a blood sample is provided, the method including: digitally staining an image of the blood sample using a trained machine learning model to generate a digitally stained image; extracting one or more intermediate features generated by the trained machine learning model during the digital staining of the image; providing the one or more extracted intermediate features to a trained multi-class classifier; and employing the trained multi-class classifier to classify at least one element in the blood sample based on the one or more extracted intermediate features. Many other embodiments are also provided.

In various examples, the present disclosure relates to using temporal filters for automated real-time classification. The technology described herein improves the performance of a multiclass classifier that may be used to classify a temporal sequence of input signals—such as input signals representative of video frames. A performance improvement may be achieved, at least in part, by applying a temporal filter to an output of the multiclass classifier. For example, the temporal filter may leverage classifications associated with preceding input signals to improve the final classification given to a subsequent signal. In some embodiments, the temporal filter may also use data from a confusion matrix to correct for the probable occurrence of certain types of classification errors. The temporal filter may be a linear filter, a nonlinear filter, an adaptive filter, and / or a statistical filter.

The present invention relates to a quantum machine learning method for multi-class classification, and the method comprises the steps of: applying a Quantum Convolution Neural Network (QCNN) quantum circuit to input data having q qubits, and outputting a feature vector based on Pauli-Z measurement; and applying a Quantum Neural Network (QNN) quantum circuit to the feature vector, and outputting a multi-class prediction vector with scalability increased compared to q qubits based on basis measurement.

A computer-implemented method can determine a linguistic boundary condition for synthetic data generation in a multi-class classification problem. The method includes analyzing empirical labelled data using linguistic and vector representation techniques. The method further includes deriving a synthetic boundary conditional (SBC) model based on the analysis of the empirical labelled data and identifying a boundary location for performant synthetic data generation using the SBC model.

The application discloses an open set image classification field self-adaption method based on self-step learning, first, the original image is preprocessed to obtain an image set, then a feature extraction module and a double multi-class classifier module are constructed and trained to align shared class features of source domain images and target domain images and separate target domain private class features, a multi-criteria cross-domain hybrid module is further constructed and trained, cross-domain hybrid images are generated by using the source domain images and the target domain images, and the shared class features are self-learned, and finally, a classification result of the target domain image is output. Compared with the existing open set image classification field self-adaption method, the application covers smooth and non-smooth class distribution, and does not need to empirically adjust the threshold for distinguishing common class images and private class images in the inference stage, so that the model has good robustness under different hyperparameters and experimental settings.

The object of this invention is to provide a multi-class classification method, a multi-class classification apparatus, and a feature selection method, a feature selection apparatus, and a feature set for such multi-class classification, all of which involve selecting feature quantities and classifying samples into any one of multiple classes based on the values ​​of the selected feature quantities. In this invention, the multi-class classification problem is addressed in conjunction with feature quantity selection. Feature quantity selection is a method of pre-selecting, literally, the feature quantities required for subsequent processing (especially multi-class classification in this invention) from a large number of feature quantities possessed by the sample. Multi-class classification is a discrimination problem that determines which of multiple classes a given unknown sample belongs to.

Methods and systems for detecting cancer and / or determining a cancer tissue of origin are disclosed. In some embodiments, a multiclass cancer classifier is disclosed that is trained with a plurality of biological samples containing cfDNA fragments. The analytics system derives a feature vector for each sample, and the multiclass classifier predicts a probability likelihood for each of a plurality of tissue of origin (TOO) classes. In some embodiments, the plurality of TOO classes include hematological subtypes, including both hematological malignancies and precursor conditions. In one embodiment, non-cancer samples having high tissue signal are pruned from the training sample set. In another embodiment, the analytics system stratifies samples according to tissue signal and applies binary threshold cutoffs determined for each stratum.

The invention provides a double-flow feature embedded driving fatigue detection system based on transfer learning, and relates to the technical field of machine vision and intelligent traffic, and the system comprises a facial feature extraction module which is used for receiving a driver facial image collected by a vehicle-mounted camera in an actual driving environment; the head posture estimation module is used for processing the head posture data set to capture the fatigue behavior performance of the driver; the migration fusion module is used for performing learnable fusion on the face visual feature vector and the head posture feature vector and converting the fusion into high-dimensional fusion feature representation; and the state classification and prompt feedback module performs multi-class classification, outputs a fatigue detection result, and is used for real-time driving state judgment and early warning. According to the method, the end-to-end heterogeneous feature embedding framework is constructed by fusing the facial representation and the three-dimensional head posture features. According to the method, efficient fusion of cross-source data features in a unified embedding space is realized, and high-reliability perception and early warning of driving fatigue behaviors are ensured.

The invention provides a pan cancer classification method based on multi-omics data and semi-supervised metric learning, and relates to the technical field of bioinformatics, and the method comprises the steps: firstly obtaining a plurality of omics data of a pan cancer sample, then carrying out the preprocessing and splicing of the multi-omics data, and obtaining a corresponding principal component score matrix through a principal component analysis method; then inputting the principal component fraction matrix into an automatic encoder network for pre-training, carrying out gene coding on a cancer sample, updating parameters of the encoder network by using part of marked data, optimizing embedded representation of the automatic encoder network by using metric learning, and finally inputting the optimized embedded representation into a constructed SVM multi-class classifier, so as to obtain an SVM multi-class classifier. Obtaining a prediction result of the unmarked sample category; compared with other methods and tests on a data set, the method provided by the invention has good performance in the aspect of category prediction of the panthenic cancer samples.

The present disclosure describes apparatuses and methods of anomaly classification. In one embodiment, an apparatus performs binary classification of data samples in a dataset to classify the data samples into a normal group or an anomaly group; performs multiclass classification to classify the data samples in the anomaly group into anomaly classes, and identifies a first set of data samples in the anomaly group as false positives resulting from the binary classification when the multiclass classification fails to classify the data samples in the first set into one of the anomaly classes.

Machine learning techniques can be applied to distinguish attacks (including enumeration attacks and account-testing attacks) from normal transaction activity. An ensemble machine learning model can include at least two generative units, one of which is trained using normal transaction data and another of which is trained using attack transaction data. Each generative unit produces a reconstructed output from a given input in a manner that reflects latent patterns in either normal or attack transactions. The reconstructed outputs and the original transaction data can be provided to as inputs to a machine learning classifier, such as a multi-label (or multi-class) classifier, that determines probability scores to different transaction types (or labels), including a first label indicating normal transactions, a second label indicating attack transactions, or a third label indicating uncertain transaction type. Based on the probability scores, the transaction can be classified as normal or attack type.

The invention relates to the technical field of ecological system monitoring and evaluation, and discloses an ecological system health evaluation method based on machine learning. The method comprises the following steps: collecting multi-source continuous monitoring data of an ecological system, and carrying out integrity check and noise filtering to generate a standardized data set; performing multi-scale feature mining on the data set, and extracting ecological feature sequences of a time domain and a frequency domain; performing community discovery on the feature sequence by adopting a spectral clustering algorithm, and outputting a clustering result of the ecological health state; constructing a multi-class classification model according to the clustering result, and adjusting the weight of the model through iterative learning; and inputting the real-time ecological data flow into the trained multi-class classification model, performing probability prediction of the health state, and automatically generating a state evaluation chart. According to the method, the health state can be adaptively defined from the data, and objective and dynamic evaluation of ecological system health is realized.

Examples relate to an image process system for markerless patella-femoral joint identification, the system comprising: a. a machine learning interface to a multiclass classification deep learning model; the machine learning interface being arranged to receive an input vector; the input vector comprising at least one, or both, of: image information and depth information associated with a patella-femoral joint; the multiclass classification deep learning model being trained to generate multiclass classification data; the multiclass classification data comprising at least: i. semantically segmented image data comprising at least one mask corresponding to a respective at least one member (distal end of femur, proximal end of tibia, patella) of the knee joint one of which being the patella, and b. a machine learning output interface of the multiclass classification deep learning model; the machine learning output interface being arranged to output the multiclass classification data.

According to an embodiment, a method for federated unsupervised domain adaptation in training a machine learning model includes an aggregator server creating a global encoder and classification head through end-to-end multi-class classifier training to minimize Mean Squared Error on its labeled data and deriving a covariance matrix from the same data. These global weights and matrix are then distributed to various local client nodes, which each send back their local weights and a covariance matrix based on their unlabeled data. The aggregator server compiles all local weights to form a new global encoder set, averages the received covariance matrices, and then retrains the model with labeled data, employing a tailored loss function that focuses on optimizing the model's performance.

Embodiments of the disclosed technologies are capable of predicting entity attributes using an Internet Protocol (IP) address. The embodiments describe obtaining an IP address. The embodiments further describe extracting routing prefixes from the IP address. The embodiments further describe performing multiclass classification using a convolutional neural network applied to the extracted routing prefixes to obtain an entity attribute. The embodiments further describe providing the entity attribute for mapping the entity attribute to digital content.

In various embodiments, systems and methods for quantum-based network traffic anomaly detection are disclosed. Embodiments for a network integrity monitor are disclosed that leverage a quantum computing-based network assessment function to evaluate network event data for the purposes of identifying and / or predicting anomalies indicative of network threats. To identify network anomalies, the network assessment function may treat the anomaly identification as a quantum search task by searching the task data using an amplitude amplification quantum search algorithm and / or using quantum machine learning models to infer a threat prediction that may include a single or multiclass classification characterizing the task data. Such classification(s) may be further assessed by the network integrity monitor as the basis to trigger one or more mitigating steps.

An image capturing device (10) for associating a target class with an object (14) is provided, wherein the image capturing device (10) has an image sensor (20) for recording image data having the object (14) and a control and evaluation unit (22) that is configured to evaluate and classify the image data using a method of machine learning, in particular a neural network, and to associate a target class with the image data. In this respect, the control and evaluation unit (22) is further configured to use as a method of machine learning a multiclass classifier for the classification into a plurality of intermediate classes that determines respective confidence values for the association of the image data with a respective intermediate class and subsequently to determine the target class by applying a map of confidence values in target classes.

A method may include applying noise to a first real image to generate a first noisy image. Then, the method may include generating a first synthetic image corresponding to an estimate of a first class of synthetic image, and computing a guidance strength of the first synthetic image based on probabilities determined from a multi-class classifier, wherein the probabilities may include a first probability of the first class of synthetic image and a second probability of a second class of synthetic image, and denoising an amount of noise determined based on the guidance strength.

Machine learning techniques may be applied to distinguish attacks, including enumeration attacks and account test attacks, from normal transaction activities. The integrated machine learning model may include at least two generation units, one of which is trained using normal transaction data and the other is trained using attack transaction data. Each generation unit generates a reconstructed output from a given input in a manner that reflects a potential pattern in a normal transaction or an aggressive transaction. The reconstructed output and raw transaction data may be provided as input to a machine learning classifier, such as a multi-tag (or multi-class) classifier, which determines probability scores for different transaction types (or tags), the different transaction types (or tags) include a first tag indicating a normal transaction, a second tag indicating an attacking transaction, or a third tag indicating an uncertain transaction type. Based on the probability score, the transaction may be classified as a normal type or an attack type.

As described herein, a system, method, and computer program are provided for a multi-stage multi-class classification. A dataset having an imbalanced distribution of data across a plurality of classes is identified. The plurality of classes are grouped into a plurality of clusters, based on a defined criteria. A plurality of machine learning models are trained, each machine learning model of the plurality of machine learning models trained using a subset of the data in the dataset corresponding to a particular cluster of the plurality of clusters. The plurality of machine learning models are used, in stages, to predict a classification for a given input.

The invention discloses a motion classification method, device and equipment based on a brain-computer interface and a storage medium, and relates to the technical field of brain-computer interfaces, and the method comprises the steps: obtaining a current target EEG signal and a current target fNIRS signal of a to-be-detected object; inputting the current target EEG signal and the current target fNIRS signal into a pre-trained mixed deep learning model, and extracting a target motion potential spatial-temporal feature and a first target abstract high-level feature with motion potential semantic information from the current target EEG signal, and extracting a target motion potential depth feature and a second target abstract high-level feature with motion potential depth information from the current target fNIRS signal, performing classification according to forward and backward time context information among all the extracted features, and outputting a classification result. According to the method, the classification accuracy of multiple types of motion tasks is improved, the generalization of brain-computer interface motion classification is improved, the implementation process is simple in calculation, and the multi-type classification performance is also high.

This disclosure relates to a method and system for multi-sensor fusion in the presence of missing and noisy labels. Prior methods for multi-sensor fusion do not estimate and correct labels for learning effective models in semi-supervised learning methods. Embodiments of the present disclosure provides a method for learning robust sensor-specific autoencoder based fusion model by utilizing a graph structure to perform label propagation and correction. In the disclosed Graph regularized AutoFuse (GAF) method latent representation for each sensor is learnt using the sensor-specific autoencoders. Further these latent representations are combined and fed to a classifier for multi-class classification. The disclosure presents a joint optimization formulation for multi-sensor fusion where label propagation and correction, sensor-specific learning and classification are executed together.

A multi-class classifier (MCC) is trained using annotated data. The annotated data comprises instances of sample data and associated label data. Creation of the annotated data and subsequent active learning by the MCC uses resources. A target-aware active learning system selects sample data for addition to an annotation queue based on factors such as current accuracy of a particular class determination and priority of that class. As each instance in the sample data in the annotation queue is annotated and used for subsequent training, accuracy of particular classes is improved until a specified accuracy for that class is attained. By being selective in the ordering of instances in the annotation queue, overall resource usage and corresponding costs associated with creating annotated data and training is reduced. Overall accuracy for all classes is improved using a smaller overall set of annotated data compared to naïve approaches.

In some embodiments, a method of classifying components of a blood sample is provided that includes digitally staining an image of a blood sample using a trained machine-learning model so as to generate a digitally-stained image; extracting one or more intermediate features generated by the trained machine-learning model during digital staining of the image; providing the one or more extracted intermediate features to a trained multi-class classifier; and employing the trained multi-class classifier to classify at least one component within the blood sample based on the one or more extracted intermediate features. Numerous other embodiments are provided.

The application discloses a one-to-many framework-based marine plankton image classification and two-stage detection method, which comprises the following steps: a feature extraction module extracts an embedded feature vector of an input image by using a convolution prototype network; a one-to-many classification module based on distance converts a multi-class classification problem into multiple binary classification subtasks, constructs a discrimination function for each class, and calculates a probability estimation of a sample belonging to each class; and a posterior fusion and hierarchical detection module fuses the probability estimation based on evidence theory, generates a posterior probability distribution containing unknown classes, and sequentially judges whether a sample is an out-of-distribution sample and an out-of-capability boundary sample through a hierarchical decision mechanism, so that joint processing of known class recognition, out-of-distribution sample detection and out-of-capability boundary sample recognition is realized.

Human activity recognition (HAR) method for determining the human activity of a user wearing a wearable device (100 ) wherein such a wearable device () comprises at least one sensor (121) which is configured to detect a movement along at least one axis of a tridimensional Cartesian reference system and to generate a corresponding detection signal, a processing and control unit (122) associated to the at least one sensor (121), said HAR method comprising the steps : -receiving the detection signals x t ,...,x t–T– 1 generated by the at least one sensor (121), where T is the size of the detection time interval and t indicates a time instant; -classifying the activity of the user by executing a HAR machine learning algorithm comprising multi-class classification task T(0) wherein the execution of said multi-class classification task T(0) is carried out by executing a plurality of n sub-tasks {T (1),..., T (n)} according to a hierarchical scheme, the execution of each one of the n sub-tasks {T (1),..., T (n)} being carried out by sequentially executing a single feature extractor (FE) module and a following respective fully connected (FC) module.

Device type discovery for a private network can be performed based on network address translated (NAT′d) network traffic generated from the network. A security solution analyzes data of network traffic from network devices using a binary classifier to determine whether the network traffic is from a NAT device. A network traffic dataset for a first time interval is preprocessed to generate a feature vector for the binary classifier, the output of which indicates whether the traffic is NAT′d. For NAT′d traffic, the security solution analyzes subsets of the network traffic dataset of smaller intervals within the first time interval. The security solution determines feature values from each network traffic data subset and generates feature vectors which are input to a multiclass classifier to obtain a device classification for each network traffic data subset.