Bibliography

A97

Schiller Medical S. A.
The Schiller ECG Measurement and Interpretation Programs Physicians Guide, 1997.

ACH$^+$89

Kenneth R. Anderson, David E. Coleman, C. Ray Hill, Andrew P. Jaworski, Patrick L. Love, Douglas A. Spindler, and Marwan Simaan.
Knowledge-based statistical process control.
In Proceedings of the First Annual Conference on Innovative Applications of Artificial Intelligence, pages pp. 23-9. American Assoc. Artificial Intelligence, 1989.

AG85

Alberto Apostolico and Zvi Galil.
Combinatorial Algorithms on Words.
NATO ASI Series. Springer-Verlag, 1985.

AK00

F. Aurenhammer and R. Klein.
In J. Sack and G. Urruita, Handbook of Computational Geometry, chapter Voronoi diagrams.
Elsevier Science, 2000.

All83

James F. Allen.
Maintaining knowledge about temporal intervals.
Communications of the ACM, 26(11):832-843, 1983.

And76

O. D. Anderson.
Time Series Analysis and Forecasting.
Butterworths, london edition, 1976.

Ben96

Yoshua Bengio.
Neural Networks for Speech and Sequence Recognition.
International Thomson Publishing Inc., 1996.

BJ76

G. E. P. Box and G. M. Jenkins.
Time Sereis Analysis: Forecasting and Control.
Holden Day, 1976.

BK99

Eric Bauer and Ronny Kohavi.
An empirical comparison of voting classification algorithms: Bagging, boosting and variants.
Machine Learning, 36:105-142, 1999.

Bra65

Ron N. Bracewell.
The Fourier Transform and Its Applications.
McGraw-Hill, New York, 1965.

Bre96

Leo Breiman.
Bagging predictors.
Machine Learning, 24:123-140, 1996.

CJ89

Paul Compton and R. Jansen.
A philosophical basis for knowledge acquisition.
In 3rd European Knowledge Acquisition for Knowledge Based Systems Workshop, 1989.

CMF90

Ron Cole, Yeshwant Muthusamy, and Mark Fanty.
The ISOLET spoken letter database.
Technical Report CS/E 90-004, Oregon Graduate Institute, 1990.

CS96

Peter Cheeseman and John Stutz.
Bayesian classification (autoclass): Theory and results.
In Advances in Knowledge Discovery and Data Mining, pages 153-180. 1996.

Dan98

Andrea Danyluk.
Predicting the Future: AI Approaches to Time-Series Problems.
AAAI Press, 1998.

DAR

DARPA.
Darpa timit database.
CD-ROM.

dC98

Philip de Chazal.
Automatic Classification of the Frank Lead Electrocardiogram.
PhD thesis, University of New South Wales, 1998.

DLM$^+$98

Gautam Das, King-Ip Lin, Heikki Mannila, Gopal Renganathan, and Padhraic Smyth.
Rule discovery from time series.
In Proceedings of the Fourth International Conference on Knowledge Discovery and Data Mining (KDD-98). AAAI Press, 1998.

DM86

T. G. Dietterich and R. S. Michalski.
Machine Learning: An Artificial Intelligence Approach, Volume II, chapter Learning to predict sequences, pages 63-106.
Morgan Kaufmann, Los Altos, CA, 1986.

FI93

Usama M. Fayyad and Keki B. Irani.
Multi-interval discretization of continuous-valued attributes for classification learning.
In IJCAI-93: Proceedings of the Thirteenth International Joint Conference on Artificial Intelligence, pages 1022-1027. Morgan-Kaufmann, 1993.

FM99

Yoav Freund and Liam Mason.
The alternating decision tree learning algorithm.
In Ivan Bratko and Saso Dzeroski, editors, Proceedins of the 16th International Conference on Machile Learning. Morgan Kaufmann, 1999.

FMR98

Nir Friedman, Kevin Murphy, and Stuart Russell.
Learning the structure of dynamic probabilistic networks.
In Proceedings Uncertainty in Artificial Intelligence Conference 1998 (UAI-98). AAAI Press, 1998.

Fri84

Jerome H. Friedman.
A variable span smoother.
Technical Report 5, Laboratory for Computational Statistics, Department of Statistics, Stanford University, 1984.

Fu82

K. Fu.
Syntactic pattern recognition and applications.
Prentice-Hall, 1982.

Geu01

Pierre Geurts.
Pattern extraction for time series classification.
In Luc de Raadt and Arno Sieves, editors, Principles of Data Mining and Knowledge Discovery, 5th European Conference, PKDD 2001, Freiburg, Germany, September 3-5, 2001, Proceedings, Lecture Notes in Computer Science. Springer-Verlag, 2001.

GRC80

G. C. Goodwin, P. J. Ramage, and P. E. Caines.
Discrete time multivariable adaptive control.
IEEE Trans. Automatic Contro, 25:449-456, 1980.

HC93

David T. Hau and Enrico W. Coiera.
Learning qualitative models of dynamic systems.
Machine Learning, 26:177-211, 1993.

HHS98

Michael Harries, Kim Horn, and Claude Sammut.
Extracting hidden context.
Machine Learning, 32(2), August 1998.

Hor93

Kim Horn.
RDR-C4: A system for monotonic knowledge acquisition and maintenance employing induction in context.
Technical report, Telstra Technical Development, 1993.

Hor01

Kurt Hornik.
The R FAQ.
http://www.ci.tuwien.ac.at/%7Ehornik/R/, 2001.

HSV92

Y. C. Ho, R. S. Sreenivas, and P. Vakili.
Ordinal optimization of DEDS.
Discrete Event Dynamic Systems: Theory and Applications, 2(1):61-88, 1992.

IB89

Nada Lavrac Ivan Bratko, Igor Mozetic.
KARDIO: A Study in Deep and Qualitative Knowledge for Expert Systems.
MIT Press, 1989.

JKP94

George H. John, Ron Kohavi, and Karl Pfleger.
Irrelevant features and the subset selection problem.
In Proceedings of the International Conference on Machile Learning 1994, pages 121-129, 1994.

Joh89

Trevor Johnston.
Auslan Dictionary: a Dictionary of the Sign Language of the Australian Deaf Community.
Deafness Resources Australia Ltd, 1989.

Kad95

Mohammed Waleed Kadous.
GRASP: Recognition of Australian sign language using instrumented gloves.
Honours Thesis, 1995.

Kad98

Mohammed Waleed Kadous.
A general architecture for supervised classification of multivariate time series.
Technical Report UNSW-CSE-TR-9806, School of Computer Science & Engineering, University of New South Wales, 1998.

Kad99

Mohammed Waleed Kadous.
Learning comprehensible descriptions of multivariate time series.
In Ivan Bratko and Saso Dzeroski, editors, Machine Learning: Proceedings of the Sixteenth International Conference (ICML '99), pages 454-463. Morgan-Kaufmann, 1999.

KCMP01

Eamonn J. Keogh, Kaushik Chakrabarti, Sharad Mehrotra, and Michael J. Pazzani.
Locally adaptive dimensionality reduction for indexing large time series databases.
In SIGMOD Conference, 2001.

KM87

K. Kumar and A. Mukerjee.
Temporal event conceptualization.
In Proc. of the 10th IJCAI, pages 472-475, Milan, Italy, 1987.

KP98

Eamonn J. Keogh and Michael J. Pazzani.
An enhanced representation of time series which allows fast and accurate classification, clustering and relevance feedback.
In Predicting the Future: AI Approaches to Time-Series Problems [Dan98], pages 44-51.

KP99

Eamonn Keogh and Michael Pazzani.
Scaling up dynamic time warping to massive datasets.
In 3rd European Conference on Principles and Practice of Knowledge Discovery in Databases, 1999.

KP01

Eamonn Keogh and Michael Pazzani.
Dynamic time warping with higher order features.
In SIAM International Conference on Data Mining, SDM 2001. SIAM, 2001.

LD94

N. Lavrac and S. Dzeroski.
Inductive Logic Programming: Techniques and Applications.
Ellis Horwood, 1994.

Lev66

V. I. Levenshtein.
Binary codes capable of correcting deletions, insertions and reversals.
Soviet Phys. Dokl., 6:707-701, 1966.

LK95

Jae Kyu Lee and Hyun Soon Kim.
Intelligent Systems for Finance and Business, chapter 13.
John Wiley and Sons Ltd, 1995.

LS98

Patrick L. Love and Marwan Simaan.
Automatic recognition of primitive changes in manufacturing process signals.
Pattern Recognition, 21(4):pp. 333-42, 1998.

Mal99

Stephane Mallat.
A wavelet tour of signal processing.
Academic Press, 1999.

Man97

Stefanos Manganaris.
Supervised Classification with Temporal Data.
PhD thesis, Computer Science Department, School of Engineering, Vanderbilt University, December 1997.

MF92

S. Muggleton and C. Feng.
Efficient induction of logic programs, 1992.

MHMW00

Takashi Matsuda, Tadashi Horiuchi, Hiroshi Motoda, and Takashi Washio.
Extension of graph-based induction for general graph structured data.
In PAKDD2000, pages 420-431, 2000.

MM98

C.J. Merz and P.M. Murphy.
UCI repository of machine learning databases, 1998.

MR81

C. S. Myers and L. R. Rabiner.
A comparative study of several dynamic time-warping algorithms for connected word recognition.
The Bell System Technical Journal, 60(7):1389-1409, September 1981.

MT91

Kouichi Murakami and Hitomi Taguchi.
Gesture recognition using recurrent neural networks.
In CHI '91 Conference Proceedings, pages 237-242. Human Interface Laboratory, Fujitsu Laboratories, ACM, 1991.

MTV95

Heikki Mannila, Hannu Toivonen, and A. Inkeri Verkamo.
Discovering frequent episodes in sequences.
In Proceedings of the First International Conference on Knowledge Discovery and Data Mining (KDD-95), pages 210-215, 1995.

Mug95

S. Muggleton.
Inverse entailment and Progol.
New Generation Computing, Special issue on Inductive Logic Programming, 13(3-4):245-286, 1995.

OG95

C.W. Omlin and C. Lee Giles.
Extraction of rules from discrete-time recurrent neural networks.
Technical Report TR 95-23, Computer Science, Troy, N.Y., August 1995.

OJC98

Tim Oates, David Jensen, and Paul R. Cohen.
Discovering rules for clustering and predicting asynchronous events.
In Predicting the Future: AI Approaches to Time-Series Problems [Dan98], pages 73-79.

OSC00

Tim Oates, Matthew D. Schmill, and Paul R. Cohen.
A method for clustering the experiences of a mobile robot that accords with human judgments.
In Proceedings 17th National Conference on Artificial Intelligence, pages 846-851. AAAI Press, 2000.

Pal97

Georgios Paliouras.
Refinement of Temporal Constraints in an Event Recognition System using Small Datasets.
PhD thesis, University of Manchester, 1997.

Paw82

Z. Pawlak.
Rough sets.
International Journal of Computer and Information Sciences, 11:341-356, 1982.

PC97

Adrian Pearce and Terry Caelli.
Schematic interpretation and the CLARET consolidated learning algorithm.
In KES '97, 1997.

Ped89

Edwin P. D. Pednault.
Some experiments in applying inductive inference to surface reconstruction.
In AAAI Conference Proceedings, pages 1603-1608. AAAI, 1989.

Qui86

J. Ross Quinlan.
Induction of decision trees.
Machine Learning, 1:81-106, 1986.

Qui90

J. Ross Quinlan.
Learning logical definitions from relations.
Machine Learning, 5:239-267, 1990.

Qui93

J. Ross Quinlan.
C4.5: Programs for Machine Learning.
Morgan Kaufmann, 1993.

RAB00

Juan J. Rodríguez, Carlos J. Alonso, and Henrik Boström.
Learning first order logic time series classifiers.
In J. Cussens and A. Frisch, editors, Proceedings of ILP2000, pages 260-275, 2000.

RC98

Michal T. Rosenstein and Paul R. Cohen.
Concepts from time series.
In AAAI '98: Fifteenth National Conference on Artificial Intelligence, pages 739-745. AAAI, AAAI Press, 1998.

RJ86

L. R. Rabiner and B. H. Juang.
An introduction to hidden markov models.
IEEE Magazine on Accoustics, Speech and Signal Processing, 3(1):4-16, 1986.

RM86

D. E. Rumelhart and J. L. McClelland.
Parallel Distributed Processing: Explorations in the microstructure of Cognition, volume 1. Foundations.
MIT Press/Bradford Books, 1986.

Sai94

Naoki Saito.
Local feature extraction and its application using a library of bases.
PhD thesis, Yale University, December 1994.

SB99

D. Suc and I. Bratko.
Modelling of control skill by qualitative constraints.
In C. Price, editor, Proceedings of the 13th International Workshop on Qualitative Reasoning, pages 212-220, University of Aberystwyth. Loch Awe, Scotland., 1999.

Sch99

Robert E. Schapire.
A brief introduction to boosting.
In Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence, 1999.

Sha88

Mildred L. G. Shaw.
Validation in a knowledge acquisition system with multiple experts.
In Proceedings of the International Conference on Fifth Generation Computer Systems, pages 1259-1266, 1988.

SM95

Yuval Shahar and Mark A. Musen.
Knowledge-based temporal abstraction in clinical domains.
Technical report, Stanford University, 1995.

SP95

Thad Starner and Alex Pentland.
Visual recognition of American Sign Language using Hidden Markov Models.
Technical Report TR-306, Media Lab, MIT, 1995.
URL: ftp://whitechapel.media.mit.edu/pub/tech-reports/TR-306.ps.Z.

Sri00

Ashwin Srinivarsan.
The Aleph manual.
Technical report, Oxford University, 2000.

SS92

Zhongping Shi and Kazuyuki Shimizu.
Neuro-fuzzy control of bioreactor systems with pattern recognition.
Journal of Fermentation and Bioengineering, 74(1):39-45, 1992.

Sta95

Thad Starner.
Visual recognition of American Sign Language using Hidden Markov Models.
Master's thesis, MIT Media Lab, Jul 1995.
URL: ftp://whitechapel.media.mit.edu/pub/tech-reports/TR-316.ps.Z.

Sta02

Statsoft.
Electronic Statistics Textbook (http://www.statsoft.com/textbook/stathome.html).
Statsoft, Tulsa, OK, 2002.

TAGD98

A. Tickle, R. Andrews, M. Golea, and J. Diederich.
The truth will come to light: Directions and challenges in extracting rules from trained neural networks.
IEEE Transactions on Neural Networks, 9:1057-1068, 1998.

Thr95

Sebastian Thrun.
In G. Tesauro, D. Touretzky and T. Leen Advances in Neural Information Processing Systems, volume 7, chapter Extracting rules from artificial neural networks with distributed representations, pages 505-512.
MIT Press, 1995.

VO99

A. Vahed and C. Omlin.
Rule extraction from recurrent neural networks using a symbolic machine learning algorithm.
In 6th International Conference on Neural Information Processing, Perth, Australia, 1999.

WALA$^+$90

J. L. Willems, C. Abreu-Lima, P. Arnaud, C.R. Brohet, and B. Denic.
Evaluation of ECG interpretation results obtained by computer and cardiologists.
Methods of Information in Medicine, 29(4):pp. 308-316, 1990.

WB68

C. Wallace and D. Boulton.
An information measure for classification, 1968.

WCS92

E. Grosse W.S. Cleveland and M. Shyu.
Statistical Models in S, chapter 8: Local Regression Models.
Brooks and Cole, 1992.

WD94

C. S. Wallace and D. L. Dowe.
Intrinsic classification by mml - the snob program.
In Proceedings of the 7th Australian Joint Conference on Artificial Intelligence, pages 37-44, 1994.

WF99

Ian H. Witten and Eibe Frank.
Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations.
Morgan Kaufmann, 1999.

Wid96

Gerhard Widmer.
Recognition and exploitation of contextual clues via incremental meta-learning.
In Proceedings of the Thirteenth International Conference on Machine Learning, pages 525-533. Morgan Kaufmann, 1996.

WL94

Allan P. White and Wei Zhong Liu.
Bias in information-based measures in decision tree induction.
Machine Learning, 15:321-329, 1994.

YKO$^+$

Steve Young, Dan Kershaw, Julian Odell, Dave Ollason, Valtcho Valtchev, and Phil Woodland.
The HTK Book.
Microsoft Corporation.

Zad65

Lotfi Zadeh.
Fuzzy sets.
Information and Control, 8:338-353, 1965.

ZR98

Geoffrey Zweig and Stuart Russell.
Speech recognition with dynamic Bayesian networks.
In Fifteenth National Conference on Artificial Intelligence (AAAI'98), pages 173-180. AAAI Press, 1998.