37 results about "Confidence set" patented technology

A computerized method for analyzing a subject URL to determine whether the subject URL is associated with a phishing attack is disclosed. The method includes steps of detecting keypoints within a screenshot of a webpage corresponding to the subject URL and determining a set of confidences based on an analysis of the detected keypoints with a model. Each confidence within the set of confidences is assigned to feature vector within a set of training feature vectors representing a training set of URLs used in generating the model. The method comprises performing an image comparison between the screenshot and a screenshot corresponding to a feature vector within the set of training feature vectors, the feature vector being assigned a highest confidence. Responsive to determining the image comparison result exceeds a predefined threshold, transmitting an alert indicating that the subject URL is associated with the phishing attack.

The technology disclosed relates to detection of anonymous proxies and bots making requests to a cloud based resource on the Internet, such as a web server or an App server. The technology can leverage one or more of: instrumentation of web pages that samples response times and other characteristics of communications by a requestor device over multiple network segments; lack of prior appearance of the requestor device across multiple, independently operated commercial web sites; and resolver usage by the requestor. These signals can be analyzed to score a requesting device's reputation. A location reported by a user device can be compared to a network characteristic determined location.

A method for checking a confidence level associated with a first user account of an online service, each user account including associated user data and contact means, the method includes the steps of selecting at least one second user account, generating a first code, recording the first code in association with the first user account, sending a first message via a contact means associated with the second user account, the first message including the first code generated and, in subsequent steps, of receiving, from a first electronic device connected to the online service with the first user account, a second message including a second code and, when the second code is equal to the first code recorded in association with the first user account, then increasing the value of the parameter indicating the confidence level associated with the first user account of the online service.

A text mining apparatus, a text mining method, and a program are provided that accurately discriminate inherent portions of each of a plurality of text data pieces including a text data piece generated by computer processing.A text mining apparatus 1 to be used performs text mining using, as targets, a plurality of text data pieces including a text data piece generated by computer processing. Confidence is set for each of the text data pieces. The text mining apparatus 1 includes an inherent portion extraction unit 6 that extracts an inherent portion of each text data piece relative to another of the text data pieces, using the confidence set for each of the text data pieces.

Embodiments of the present invention provide an image processing method. The method includes: extracting image features of the image to be processed to obtain a first eigenvalue matrix; using a classification prediction model to process the first eigenvalue matrix, and determining the image to be processed relative to each of a plurality of predetermined angle categories The prediction confidence of the predetermined angle category and generate a prediction confidence set, wherein the predetermined angle category indicates the angle interval where the offset angle is located; according to the prediction confidence set, determine the offset angle of the image to be processed; and according to the set The offset angle is used to rotate the image to be processed. The method of the invention converts the problem of determining the offset angle of an image into an angle classification task, which can effectively reduce the computational complexity and improve the accuracy of the determined offset angle. In addition, the embodiments of the present invention also provide an image processing device, a medium, and a computing device.

The invention discloses an online cooperative positioning method and system based on Wi-Fi RSS, belonging to the technical field of communication and wireless network. The present invention uses the RSS of other users who request positioning at the same time to obtain the collaborative confidence of all candidate reference points of the target user, and enriches the positioning information without adding additional hardware devices, thereby improving the positioning accuracy; integrating the signal distance of the user itself and collaborative confidence, according to the final comprehensive confidence of the candidate reference point, determine the final positioning result of the target user, increase the reliability and accuracy of positioning; by eliminating the signal source with low regional acceptance rate or weak overall signal strength, thereby Eliminate remote signal sources and temporary hotspot sources. Such invalid signal sources will often interfere with the comparison of fingerprints and improve positioning accuracy; standardize the signal distance set and the collaborative confidence set to eliminate the two The dimensional influence of the indicator increases the positioning accuracy.

The invention discloses a hyperspectral image semi-supervised classification method based on comprehensive confidence. The method comprises the following steps: reading a hyperspectral image; Calculating a graph weight matrix; 8, performing adjacent connection on the sparse graph weight matrix; Calculating a normalized graph weight matrix; Obtaining an initial training set and a candidate set; Setting collaborative training iteration times and starting a training process; Training a polynomial logic regression classifier; Obtaining a prediction label of the candidate set sample by using a polynomial logic regression classifier; Obtaining prediction tags of the candidate set samples by using a semi-supervised graph classification method; Selecting two candidate samples with consistent prediction tags and corresponding prediction tags to form a protocol set, and forming a comprehensive confidence set by corresponding confidence coefficients; Screening out a protocol set sample with a comprehensive confidence coefficient higher than 99% and a corresponding prediction label, and forming an amplification set and adding the amplification set into a training set; Removing an amplificationset sample in the candidate set; And judging whether the training reaches a set number of times, if not, continuing iteration, and if yes, stopping iteration, and classifying the hyperspectral imagesby using the semi-supervised graph.