Robert L. Rounthwaite

TL;DR: This work describes a graphical model for probabilistic relationships--an alternative to the Bayesian network--called a dependency network and identifies several basic properties of this representation and describes a computationally efficient procedure for learning the graph and probability components from data.

TL;DR: In this paper, a machine learning approach is applied to spam filters, and examples of both legitimate and junk/spam mail are obtained to generate sets of training data to classify items in connection with spam prevention in server or client-based architectures.

TL;DR: In this paper, a system and method that facilitate extracting data from messages for spam filtering is presented, where the extracted data can be in the form of features, which can be employed in connection with machine learning systems to build improved filters.

Abstract: Phishing detection, prevention, and notification is described. In an embodiment, a messaging application facilitates communication via a messaging user interface, and receives a communication, such as an email message, from a domain. A phishing detection module detects a phishing attack in the communication by determining that the domain is similar to a known phishing domain, or by detecting suspicious network properties of the domain. In another embodiment, a Web browsing application receives content, such as data for a Web page, from a network-based resource, such as a Web site or domain. The Web browsing application initiates a display of the content, and a phishing detection module detects a phishing attack in the content by determining that a domain of the network-based resource is similar to a known phishing domain, or that an address of the network-based resource from which the content is received has suspicious network properties.

TL;DR: In this paper, a statistical language model is generated from the text data and a speech recognition operation is performed on the audio data using the generated language model and a speaker independent acoustic model.