Philip K. Chan

发表

The ALEXSYS mortgage pool allocation system

Salvatore J. Stolfo, Philip K. Chan, Leland Woodbury, 1991, Proceedings First International Conference on Artificial Intelligence Applications on Wall Street.

Learning a Neural-network-based Representation for Open Set Recognition pdf

Philip K. Chan, Mehadi Hassen, P. Chan, 2018, SDM.

JAM: Java Agents for Meta-Learning over Distributed Databases

Salvatore J. Stolfo, Philip K. Chan, Wenke Lee, 1997, KDD.

Detecting Harmful Hand Behaviors with Machine Learning from Wearable Motion Sensor Data

Philip K. Chan, Lingfeng Zhang, 2018, FLAIRS Conference.

Machine Learning for IT Security

Philip K. Chan, P. Chan, 2010, Encyclopedia of Machine Learning and Data Mining.

Increasing coverage to improve detection of network and host anomalies

Philip K. Chan, Gaurav Tandon, P. Chan, 2010, Machine Learning.

An Analysis of the 1999 DARPA/Lincoln Laboratory Evaluation Data for Network Anomaly Detection

Philip K. Chan, Matthew V. Mahoney, M. Mahoney, 2003, RAID.

On the Learning of System Call Attributes for Host-based Anomaly Detection

Philip K. Chan, Gaurav Tandon, 2006, Int. J. Artif. Intell. Tools.

Learning rules for anomaly detection of hostile network traffic

Philip K. Chan, Matthew V. Mahoney, M. Mahoney, 2003, Third IEEE International Conference on Data Mining.

Cost-based modeling for fraud and intrusion detection: results from the JAM project

Salvatore J. Stolfo, Philip K. Chan, Wei Fan, 2000, Proceedings DARPA Information Survivability Conference and Exposition. DISCEX'00.

Experiments on multistrategy learning by meta-learning

Salvatore J. Stolfo, Philip K. Chan, P. Chan, 1993, CIKM '93.

Toward Scalable Learning with Non-uniform Distributions: Effects and a Multi-classifier Approach

Philip K. Chan, 1999 .

Toward Multi-Strategy Parallel & Distributed Learning in Sequence Analysis

Salvatore J. Stolfo, Philip K. Chan, 1993, ISMB.

Motif-Oriented Representation of Sequences for a Host-Based Intrusion Detection System

Philip K. Chan, Debasis Mitra, Gaurav Tandon, 2004, IEA/AIE.

Scalable Function Call Graph-based Malware Classification

Philip K. Chan, Mehadi Hassen, P. Chan, 2017, CODASPY.

Estimating Hospital Admissions with a Randomized Regression Approach

Philip K. Chan, Kleber A. Garcia, P. Chan, 2012, 2012 11th International Conference on Machine Learning and Applications.

Machine Learning for Computer Security

Philip K. Chan, Richard Lippmann, R. Lippmann, 2006, J. Mach. Learn. Res..

Credit Card Fraud Detection using Neural Network

Chang-Tien Lu, Philip K. Chan, Wei Fan, 2017 .

PARULE: Parallel Rule Processing Using Meta-rules for Redaction

Salvatore J. Stolfo, Philip K. Chan, Leland Woodbury, 1991, J. Parallel Distributed Comput..

Machine Learning for IT Security

Philip K. Chan, P. Chan, 2010, Encyclopedia of Machine Learning and Data Mining.

Semantic Search Techniques for Learning Smaller Boolean Expression Trees in Genetic Programming

Philip K. Chan, Nicholas C. Miller, 2014, Int. J. Comput. Intell. Appl..

Improving learning implicit user interest hierarchy with variable length phrases

Philip K. Chan, Hyoung-rae Kim, P. Chan, 2003 .

Learning to Identify Known and Unknown Classes: A Case Study in Open World Malware Classification

Philip K. Chan, Mehadi Hassen, P. Chan, 2018, FLAIRS.

Toward Scalable Learning with Non-Uniform Class and Cost Distributions: A Case Study in Credit Card Fraud Detection

Salvatore J. Stolfo, Philip K. Chan, P. Chan, 1998, KDD.

Estimating effectiveness of twitter messages with a personalized machine learning approach

Philip K. Chan, Xunhu Sun, P. Chan, 2018, Knowledge and Information Systems.

Machine Learning in Molecular Biology Sequence Analysis

Philip K. Chan, P. Chan, 1991 .

Using artificial anomalies to detect unknown and known network intrusions

Salvatore J. Stolfo, Philip K. Chan, Wei Fan, 2001, Proceedings 2001 IEEE International Conference on Data Mining.

Constructing Web User Profiles: A non-invasive Learning Approach

Philip K. Chan, P. Chan, 1999, WEBKDD.

MMF: A loss extension for feature learning in open set recognition pdf

Philip K. Chan, Jingyun Jia, 2020, ArXiv.

Learning Rules and Clusters for Anomaly Detection in Network Traffic

Philip K. Chan, Matthew V. Mahoney, Muhammad H. Arshad, 2005 .

Meta-learning in distributed data mining systems: Issues and approaches

Philip K. Chan, Andreas L. Prodromidis, P. Chan, 2007 .

Mining pros and cons of actions from social media for decision support

Philip K. Chan, Ebad Ahmadzadeh, P. Chan, 2017, 2017 IEEE International Conference on Big Data (Big Data).

Distributed data mining in credit card fraud detection

Salvatore J. Stolfo, Philip K. Chan, Wei Fan, 1999, IEEE Intell. Syst..

Statistical guidance in symbolic learning

Philip K. Chan, Douglas H. Fisher, P. Chan, 1990, Annals of Mathematics and Artificial Intelligence.

Learning implicit user interest hierarchy for context in personalization

Philip K. Chan, Hyoung-rae Kim, P. Chan, 2008, IUI '03.

Systems for Knowledge Discovery in Databases

Philip K. Chan, Gregory Piatetsky-Shapiro, Christopher J. Matheus, 1993, IEEE Trans. Knowl. Data Eng..

Learning implicit user interest hierarchy for web personalization

Philip K. Chan, Hyoung-rae Kim, P. Chan, 2005 .

Personalized Search Results with User Interest Hierarchies Learnt from Bookmarks

Philip K. Chan, Hyoung-rae Kim, P. Chan, 2005, WEBKDD.

AdaCost: Misclassification Cost-Sensitive Boosting

Salvatore J. Stolfo, Philip K. Chan, Wei Fan, 1999, ICML.

Incrementally Learning Rules for Anomaly Detection

Philip K. Chan, Denis Petrussenko, P. Chan, 2010, FLAIRS.

An Analysis of Instance Selection for Neural Networks to Improve Training Speed

Philip K. Chan, Xunhu Sun, 2014, 2014 13th International Conference on Machine Learning and Applications.

Tracking User Mobility to Detect Suspicious Behavior

Philip K. Chan, Gaurav Tandon, 2009, SDM.

On the Accuracy of Meta-learning for Scalable Data Mining

Salvatore J. Stolfo, Philip K. Chan, P. Chan, 2004, Journal of Intelligent Information Systems.

Identifying Pros and Cons of Product Aspects Based on Customer Reviews

Philip K. Chan, Ebad Ahmadzadeh, 2018, 2018 IEEE International Conference on Big Data (Big Data).

Identifying variable-length meaningful phrases with correlation functions

Philip K. Chan, Hyoung-rae Kim, P. Chan, 2004, 16th IEEE International Conference on Tools with Artificial Intelligence.

Improving efficiency of maximizing spread in the flow authority model for large sparse networks

Philip K. Chan, Ebad Ahmadzadeh, 2016, 2016 IEEE International Conference on Big Data (Big Data).

Learning Rules from System Call Arguments and Sequences for Anomaly 20 Detection

Salvatore J. Stolfo, Angelos D. Keromytis, Robert K. Cunningham, 2003 .

Credit Card Fraud Detection Using Meta-Learning: Issues and Initial Results 1

Salvatore J. Stolfo, Philip K. Chan, Wenke Lee, 1997 .

Weighting versus pruning in rule validation for detecting network and host anomalies

Philip K. Chan, Gaurav Tandon, P. Chan, 2007, KDD '07.

Data mining-based intrusion detectors: an overview of the columbia IDS project

Salvatore J. Stolfo, Philip K. Chan, Wei Fan, 2001, SGMD.

Personalized Ranking of Search Results with Learned User Interest Hierarchies from Bookmarks

Philip K. Chan, Hyoung-rae Kim, 2005 .

A Comparative Evaluation of Voting and Meta-learning on Partitioned Data

Salvatore J. Stolfo, Philip K. Chan, P. Chan, 1995, ICML.

Sharing Learned Models among Remote Database Partitions by Local Meta-Learning

Salvatore J. Stolfo, Philip K. Chan, 1996, KDD.

Modeling multiple time series for anomaly detection

Philip K. Chan, Matthew V. Mahoney, M. Mahoney, 2005, Fifth IEEE International Conference on Data Mining (ICDM'05).

MORPHEUS: motif oriented representations to purge hostile events from unlabeled sequences

Philip K. Chan, Debasis Mitra, Gaurav Tandon, 2004, VizSEC/DMSEC '04.

Personalized ranking of search results with implicitly learned user interest hierarchies

Philip K. Chan, Hyoung-rae Kim, 2005 .

Using artificial anomalies to detect unknown and known network intrusions

Salvatore J. Stolfo, Philip K. Chan, Wei Fan, 2003, Knowledge and Information Systems.

Implicit Indicators for Interesting Web Pages

Philip K. Chan, Hyoung-rae Kim, 2005, WEBIST.

Using A Personalized Anomaly Detection Approach with Machine Learning to Detect Stolen Phones

Philip K. Chan, Huizhong Hu, 2018, FLAIRS Conference.

Malware classification using static analysis based features

Philip K. Chan, Mehadi Hassen, Marco M. Carvalho, 2017, 2017 IEEE Symposium Series on Computational Intelligence (SSCI).

Inductive Learning with BCT

Philip K. Chan, P. Chan, 1989, ML.

Intelligent Systems at Florida Tech

Philip K. Chan, Ronaldo Menezes, Marius-Calin Silaghi, 2007, IEEE Intell. Informatics Bull..

Learning Arbiter and Combiner Trees from Partitioned Data for Scaling Machine Learning

Salvatore J. Stolfo, Philip K. Chan, P. Chan, 1995, KDD.

Advances in Distributed and Parallel Knowledge Discovery

Philip K. Chan, Hillol Kargupta, H. Kargupta, 2000 .

Using demographics to understand better the students? behavior, improving the performance prediction in online courses

Philip K. Chan, Andre Kuroswiski, 2017 .

Guest Editors' Introduction

Salvatore J. Stolfo, Philip K. Chan, David H. Wolpert, 2004, Machine Learning.

Learning nonstationary models of normal network traffic for detecting novel attacks

Philip K. Chan, Matthew V. Mahoney, M. Mahoney, 2002, KDD.

Learning implicit user interest hierarchy for context in personalization

Philip K. Chan, Hyoung R. Kim, 2003, IUI.

Learning Useful System Call Attributes for Anomaly Detection

Philip K. Chan, Gaurav Tandon, 2005, FLAIRS Conference.

Self-supervised Detransformation Autoencoder for Representation Learning in Open Set Recognition pdf

Philip K. Chan, Jingyun Jia, 2021, ArXiv.

MMF: A loss extension for feature learning in open set recognition

Philip K. Chan, Jingyun Jia, 2021, ICANN.

SensorMiner : Tool Kit for Anomaly Detection in Physical Time Series ( Industrial Track Submission

Philip K. Chan, 2006 .