"Advanced statistical modeling and knowledge representation techniques for a newly emerging area of machine learning and probabilistic reasoning; includes introductory material, tutorials for different proposed approaches, and applications.Handling inherent uncertainty and exploiting compositional structure are fundamental to understanding and designing large-scale systems. Statistical relational learning builds on ideas from probability ..."
"Handling inherent uncertainty and exploiting compositional structure are fundamental to understanding and designing large-scale systems. Statistical relational learning builds on ideas from probability theory and statistics to address uncertainty while incorporating tools from logic, databases and programming languages to represent structure. In Introduction to Statistical ..."
"This synthesis lecture provides a survey of work on privacy in online social networks (OSNs). This work encompasses concerns of users as well as service providers and third parties. Our goal is to approach such concerns from a computer-science perspective, and building upon existing work on privacy, security, statistical modeling and databases to provide an overview of the technical and algorithmic issues related to privacy in OSNs. We ..."
"What is that elusive quality, that X factor that makes some people wildly successful? By asking some of the most notable leaders in the world, actor, author, and producer Sean Kanan and entrepreneur, author, and motivational speaker Jill Liberman might just have found the answers. In this compilation, sit at the feet of Tony Robbins, Mark Cuban, Jason Alexander, Sara Blakely, Don King, and more to learn the truth about success."
"Handling inherent uncertainty and exploiting compositional structure are fundamental to understanding and designing large-scale systems. Statistical relational learning builds on ideas from probability theory and statistics to address uncertainty while incorporating tools from logic, databases and programming languages to represent structure. In Introduction to Statistical ..."
"This book presents pattern-based problem-solving methods for a variety of machine learning and data analysis problems. The methods are all based on techniques that exploit the power of group differences. They make use of group differences represented using emerging patterns (aka contrast patterns), which are patterns that match significantly different numbers of instances in different data groups. A large number of applications outside ..."
"The real-world data, though massive, is largely unstructured, in the form of natural-language text. It is challenging but highly desirable to mine structures from massive text data, without extensive human annotation and labeling. In this book, we investigate the principles and methodologies of mining structures of factual knowledge (e.g., entities and their relationships) from massive, unstructured text corpora. Departing from many exi ..."
"We would like to thank all of our workshop participants for making the event a
success. We would also like to thank Ron Baecker and Diane Cerra, on the
publishing side, for their support and advice. WORKSHOP COMMITTEE Gregory
Abowd, ..."
"Adding the links between the one-hop neighbors of a node, sometimes referred
to as the 1.5-hop neighborhood, creates a richer structural signature. Based on
this, Ana and Don still have the same subgraph signature, and so do Emma and
Fabio. However, Gina has a unique signature and is easily identifiable by an
adversary who has knowledge ofher true 1.5-hop neighborhood structure. Zhou
and Pei [107] formalize the desired property to ..."
KDD-2003 proceedings of the Ninth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, August 24-27, 2003, Washington, DC, USA by Ted Senator, LiseGetoor, Pedro Domingos, Christos Faloutsos Paperback, 762 Pages, Published 2003 by Assn For Computing Machinery ISBN-13: 978-1-58113-737-8, ISBN: 1-58113-737-0
"The problem is that such a sample may not adequately represent the entire data
set due to random fluctuations in the sampling process. This difficulty is
particularly apparent at. small sample sizes. In this paper we introduce a novel
data-reduction method, called EASE (Epsilon Approximation: Sampling Enabled)
, that is especially designed for categorical count data.' This algorithm 59 Efficient
Data Reduction with EASE Herve Bronni ..."
Exploiting the Power of Group Differences Using Patterns to Solve Data Analysis Problems (Synthesis Lectures on Data Mining and Knowledge Discovery) by Guozhu Dong, Jiawei Han, LiseGetoor Paperback, 148 Pages, Published 2019 by Morgan & Claypool Publishers ISBN-13: 978-1-68173-502-3, ISBN: 1-68173-502-4
"This book presents pattern-based problem-solving methods for a variety of machine learning and data analysis problems. The methods are all based on techniques that exploit the power of group differences. They make use of group differences represented using emerging patterns (aka contrast patterns), which are patterns that match significantly different numbers of instances in different data groups. A large number of applications outside ..."
"The real-world data, though massive, is largely unstructured, in the form of natural-language text. It is challenging but highly desirable to mine structures from massive text data, without extensive human annotation and labeling. In this book, we investigate the principles and methodologies of mining structures of factual knowledge (e.g., entities and their relationships) from massive, unstructured text corpora. Departing from many exi ..."
"Graphs naturally represent information ranging from links between web pages, to communication in email networks, to connections between neurons in our brains. These graphs often span billions of nodes and interactions between them. Within this deluge of interconnected data, how can we find the most important structures and summarize them? How can we efficiently visualize them? How can we detect anomalies that indicate critical events, s ..."
"Graphs naturally represent information ranging from links between web pages, to communication in email networks, to connections between neurons in our brains. These graphs often span billions of nodes and interactions between them. Within this deluge of interconnected data, how can we find the most important structures and summarize them? How can we efficiently visualize them? How can we detect anomalies that indicate critical events, s ..."
"Knowledge Discovery from Databases (KDD) and Data Mining (DM) are general terms for a research area that deals with constructing models from data for predictive, descriptive, or summarizing purposes. Existing text books provide a broad overview of these problems and usually devote most attention to topics such as clustering and classification. Their discussions of pattern mining are often restricted to the basics of itemset mining and a ..."
"Social media shatters the barrier to communicate anytime anywhere for people of all walks of life. The publicly available, virtually free information in social media poses a new challenge to consumers who have to discern whether a piece of information published in social media is reliable. For example, it can be difficult to understand the motivations behind a statement passed from one user to another, without knowing the person who ori ..."