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Philip Chan 0001
Person information
- affiliation: Florida Institute of Technology, Department of Computer Sciences, Melbourne, FL, USA
Other persons with the same name
- Philip Chan — disambiguation page
- Philip Chan 0002 — Monash University, Caulfield School of Information Technology, VIC, Australia
- Philip Chan 0003 — University of Queensland, Brisbane, Australia
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2020 – today
- 2023
- [c44]Jingyun Jia
, Philip K. Chan
GII: A Unified Approach to Representation Learning in Open Set Recognition with Novel Category Discovery. ICANN (8) 2023: 243-254 - [c43]Jingyun Jia, Philip K. Chan:
Feature Decoupling in Self-supervised Representation Learning for Open Set Recognition. IJCNN 2023: 1-8 - 2022
- [c42]Jingyun Jia
Self-supervised Detransformation Autoencoder for Representation Learning in Open Set Recognition. ICANN (4) 2022: 471-483 - [c41]Jingyun Jia, Philip K. Chan:
Representation Learning with Function Call Graph Transformations for Malware Open Set Recognition. IJCNN 2022: 1-8 - [i5]Jingyun Jia, Philip K. Chan:
Representation learning with function call graph transformations for malware open set recognition. CoRR abs/2205.06918 (2022) - [i4]Jingyun Jia, Philip K. Chan:
Feature Decoupling in Self-supervised Representation Learning for Open Set Recognition. CoRR abs/2209.14385 (2022) - 2021
- [c40]Jingyun Jia
MMF: A Loss Extension for Feature Learning in Open Set Recognition. ICANN (2) 2021: 319-331 - [i3]Jingyun Jia, Philip K. Chan:
Self-supervised Detransformation Autoencoder for Representation Learning in Open Set Recognition. CoRR abs/2105.13557 (2021) - 2020
- [c39]Mehadi Hassen, Philip K. Chan:
Unsupervised Open Set Recognition using Adversarial Autoencoders. ICMLA 2020: 360-365 - [c38]Mehadi Hassen, Philip K. Chan:
Learning a Neural-network-based Representation for Open Set Recognition. SDM 2020: 154-162 - [i2]Jingyun Jia, Philip K. Chan:
MMF: A loss extension for feature learning in open set recognition. CoRR abs/2006.15117 (2020)
2010 – 2019
- 2018
- [j18]Xunhu Sun
Estimating effectiveness of twitter messages with a personalized machine learning approach. Knowl. Inf. Syst. 56(1): 27-53 (2018) - [c37]Ebad Ahmadzadeh, Philip K. Chan
Identifying Pros and Cons of Product Aspects Based on Customer Reviews. IEEE BigData 2018: 931-936 - [c36]Mehadi Hassen, Philip K. Chan:
Learning to Identify Known and Unknown Classes: A Case Study in Open World Malware Classification. FLAIRS 2018: 26-31 - [c35]Lingfeng Zhang, Philip K. Chan:
Detecting Harmful Hand Behaviors with Machine Learning from Wearable Motion Sensor Data. FLAIRS 2018: 323-328 - [c34]Huizhong Hu, Philip K. Chan:
Using A Personalized Anomaly Detection Approach with Machine Learning to Detect Stolen Phones. FLAIRS 2018: 410-415 - [i1]Mehadi Hassen, Philip K. Chan:
Learning a Neural-network-based Representation for Open Set Recognition. CoRR abs/1802.04365 (2018) - 2017
- [c33]Ebad Ahmadzadeh, Philip K. Chan
Mining pros and cons of actions from social media for decision support. IEEE BigData 2017: 877-882 - [c32]Mehadi Hassen, Philip K. Chan:
Scalable Function Call Graph-based Malware Classification. CODASPY 2017: 239-248 - [c31]Mehadi Hassen, Marco M. Carvalho
Malware classification using static analysis based features. SSCI 2017: 1-7 - [r2]Philip K. Chan:
Machine Learning for IT Security. Encyclopedia of Machine Learning and Data Mining 2017: 788-790 - 2016
- [c30]Philip K. Chan, Ebad Ahmadzadeh:
Improving efficiency of maximizing spread in the flow authority model for large sparse networks. IEEE BigData 2016: 863-868 - 2014
- [j17]Nicholas C. Miller, Philip K. Chan
Semantic Search Techniques for Learning Smaller Boolean Expression Trees in Genetic Programming. Int. J. Comput. Intell. Appl. 13(3) (2014) - [c29]Xunhu Sun, Philip K. Chan
An Analysis of Instance Selection for Neural Networks to Improve Training Speed. ICMLA 2014: 288-293 - 2012
- [c28]Kleber A. Garcia, Philip K. Chan
Estimating Hospital Admissions with a Randomized Regression Approach. ICMLA (1) 2012: 179-184 - 2010
- [j16]Gaurav Tandon, Philip K. Chan
Increasing coverage to improve detection of network and host anomalies. Mach. Learn. 79(3): 307-334 (2010) - [c27]Denis Petrussenko, Philip K. Chan:
Incrementally Learning Rules for Anomaly Detection. FLAIRS 2010 - [r1]Philip K. Chan
Machine Learning for IT Security. Encyclopedia of Machine Learning 2010: 637-639
2000 – 2009
- 2009
- [c26]Gaurav Tandon, Philip K. Chan:
Tracking User Mobility to Detect Suspicious Behavior. SDM 2009: 871-882 - 2008
- [j15]Hyoung-rae Kim, Philip K. Chan
Learning implicit user interest hierarchy for context in personalization. Appl. Intell. 28(2): 153-166 (2008) - 2007
- [j14]Philip K. Chan, Ronaldo Menezes, Debasis Mitra, Eraldo Ribeiro, Marius Silaghi:
Intelligent Systems at Florida Tech. IEEE Intell. Informatics Bull. 8(1): 5-6 (2007) - [j13]Stan Salvador, Philip Chan:
Toward accurate dynamic time warping in linear time and space. Intell. Data Anal. 11(5): 561-580 (2007) - [c25]Gaurav Tandon, Philip K. Chan
Weighting versus pruning in rule validation for detecting network and host anomalies. KDD 2007: 697-706 - 2006
- [j12]Gaurav Tandon, Philip K. Chan
On the Learning of System Call Attributes for Host-based Anomaly Detection. Int. J. Artif. Intell. Tools 15(6): 875-892 (2006) - [j11]Philip K. Chan, Richard Lippmann:
Machine Learning for Computer Security. J. Mach. Learn. Res. 7: 2669-2672 (2006) - 2005
- [j10]Stan Salvador, Philip Chan
Learning States and Rules for Detecting Anomalies in Time Series. Appl. Intell. 23(3): 241-255 (2005) - [j9]Dragos D. Margineantu, Stephen Bay, Philip Chan, Terran Lane:
Data mining methods for anomaly detection KDD-2005 workshop report. SIGKDD Explor. 7(2): 132-136 (2005) - [c24]Gaurav Tandon, Philip K. Chan:
Learning Useful System Call Attributes for Anomaly Detection. FLAIRS 2005: 405-411 - [c23]Philip K. Chan
Modeling Multiple Time Series for Anomaly Detection. ICDM 2005: 90-97 - [c22]Hyoung-rae Kim, Philip K. Chan:
Personalized Search Results with User Interest Hierarchies Learnt from Bookmarks. WEBKDD 2005: 158-176 - [c21]Hyoung-rae Kim, Philip K. Chan:
Implicit Indicators for Interesting Web Pages. WEBIST 2005: 270-277 - 2004
- [j8]Wei Fan, Matthew Miller, Salvatore J. Stolfo, Wenke Lee, Philip K. Chan:
Using artificial anomalies to detect unknown and known network intrusions. Knowl. Inf. Syst. 6(5): 507-527 (2004) - [c20]Stan Salvador, Philip Chan, John Brodie:
Learning States and Rules for Time Series Anomaly Detection. FLAIRS 2004: 306-311 - [c19]Hyoung-rae Kim, Philip K. Chan:
Identifying Variable-Length Meaningful Phrases with Correlation Functions. ICTAI 2004: 30-38 - [c18]Stan Salvador, Philip Chan
Determining the Number of Clusters/Segments in Hierarchical Clustering/Segmentation Algorithms. ICTAI 2004: 576-584 - [c17]Gaurav Tandon, Debasis Mitra
Motif-Oriented Representation of Sequences for a Host-Based Intrusion Detection System. IEA/AIE 2004: 605-615 - [c16]Gaurav Tandon, Philip K. Chan, Debasis Mitra:
MORPHEUS: motif oriented representations to purge hostile events from unlabeled sequences. VizSEC 2004: 16-25 - [e1]Carla E. Brodley, Philip Chan, Richard Lippmann, William Yurcik:
1st ACM Workshop on Visualization and Data Mining for Computer Security, VizSEC/DMSEC 2004, Washington, DC, USA, October 29, 2004. ACM 2004, ISBN 1-58113-974-8 [contents] - 2003
- [c15]Matthew V. Mahoney, Philip K. Chan:
Learning Rules for Anomaly Detection of Hostile Network Traffic. ICDM 2003: 601-604 - [c14]Hyoung R. Kim, Philip K. Chan:
Learning implicit user interest hierarchy for context in personalization. IUI 2003: 101-108 - [c13]Matthew V. Mahoney, Philip K. Chan:
An Analysis of the 1999 DARPA/Lincoln Laboratory Evaluation Data for Network Anomaly Detection. RAID 2003: 220-237 - 2002
- [c12]Matthew V. Mahoney, Philip K. Chan:
Learning nonstationary models of normal network traffic for detecting novel attacks. KDD 2002: 376-385 - 2001
- [j7]Salvatore J. Stolfo, Wenke Lee, Philip K. Chan, Wei Fan, Eleazar Eskin:
Data Mining-based Intrusion Detectors: An Overview of the Columbia IDS Project. SIGMOD Rec. 30(4): 5-14 (2001) - [c11]Wei Fan, Matthew Miller, Salvatore J. Stolfo, Wenke Lee, Philip K. Chan:
Using Artificial Anomalies to Detect Unknown and Known Network Intrusions. ICDM 2001: 123-130
1990 – 1999
- 1999
- [j6]Philip Chan, Wei Fan, Andreas L. Prodromidis, Salvatore J. Stolfo:
Distributed data mining in credit card fraud detection. IEEE Intell. Syst. 14(6): 67-74 (1999) - [j5]Philip K. Chan, Salvatore J. Stolfo, David H. Wolpert:
Guest Editors' Introduction. Mach. Learn. 36(1-2): 5-7 (1999) - [c10]Wei Fan, Salvatore J. Stolfo, Junxin Zhang, Philip K. Chan:
AdaCost: Misclassification Cost-Sensitive Boosting. ICML 1999: 97-105 - [c9]Philip K. Chan:
Constructing Web User Profiles: A non-invasive Learning Approach. WEBKDD 1999: 39-55 - 1998
- [c8]Philip K. Chan, Salvatore J. Stolfo:
Toward Scalable Learning with Non-Uniform Class and Cost Distributions: A Case Study in Credit Card Fraud Detection. KDD 1998: 164-168 - [d1]Salvatore J. Stolfo, Wei Fan, Wenke Lee, Andreas L. Prodromidis, Philip Chan:
KDD Cup 1999 Data. UCI Machine Learning Repository, 1998 - 1997
- [j4]Philip K. Chan, Salvatore J. Stolfo:
On the Accuracy of Meta-Learning for Scalable Data Mining. J. Intell. Inf. Syst. 8(1): 5-28 (1997) - [c7]Salvatore J. Stolfo, Andreas L. Prodromidis, Shelley Tselepis, Wenke Lee, Dave W. Fan, Philip K. Chan:
JAM: Java Agents for Meta-Learning over Distributed Databases. KDD 1997: 74-81 - 1996
- [c6]Philip K. Chan, Salvatore J. Stolfo:
Sharing Learned Models among Remote Database Partitions by Local Meta-Learning. KDD 1996: 2-7 - 1995
- [c5]Philip K. Chan, Salvatore J. Stolfo:
A Comparative Evaluation of Voting and Meta-learning on Partitioned Data. ICML 1995: 90-98 - [c4]Philip K. Chan, Salvatore J. Stolfo:
Learning Arbiter and Combiner Trees from Partitioned Data for Scaling Machine Learning. KDD 1995: 39-44 - 1993
- [j3]Christopher J. Matheus, Philip K. Chan
Systems for Knowledge Discovery in Databases. IEEE Trans. Knowl. Data Eng. 5(6): 903-913 (1993) - [c3]Philip K. Chan, Salvatore J. Stolfo:
Experiments on Multi-Strategy Learning by Meta-Learning. CIKM 1993: 314-323 - [c2]Philip K. Chan, Salvatore J. Stolfo:
Toward Multi-Strategy Parallel & Distributed Learning in Sequence Analysis. ISMB 1993: 65-73 - 1991
- [j2]Salvatore J. Stolfo, Ouri Wolfson
PARULE: Parallel Rule Processing Using Meta-rules for Redaction. J. Parallel Distributed Comput. 13(4): 366-382 (1991) - 1990
- [j1]Douglas H. Fisher, Philip K. Chan
Statistical guidance in symbolic learning. Ann. Math. Artif. Intell. 2: 135-147 (1990)
1980 – 1989
- 1989
- [c1]Philip K. Chan:
Inductive Learning with BCT. ML 1989: 104-108
Coauthor Index
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