Seminaari: Koneoppiminen (2 ov)

Tapio Elomaa
Jyrki Kivinen

Kevät 2000, 18.1.-2.5.

ti 14-16, B453

Yleistä

Seminaarissa tarkastellaan sekä empiirisen että teoreettisen koneoppimistutkimuksen piirissä saavutettuja uudehkoja, analyyttisesti perusteltuja tuloksia.

Esitiedot

Riittävät matemaattiset valmiudet. Kurssien tekoäly, koneoppiminen ja robotiikka suorittamisesta on hyötyä.

Muodot

Kirjallinen / elektroninen materiaali toimitettava ennakkoon. Mielellään noin viikko ennen esitelmää.

Aikataulu

18. 1.:	Järjestäytyminen
25. 1.:	Ei tapaamista
1. 2.:	Juho Rousu: Numeeristen arvoalueiden osittaminen luokittelun oppimisessa
	T. Elomaa & J. Rousu: General and efficient multisplitting of numerical attributes. Machine Learning 36 (1999) 201-244. J. R. Quinlan: Induction of decision trees. Machine Learning 1 (1986) 81-106.
8. 2.:	Taneli Mielikäinen: Boosting - Taustaa: PAC-malli ja VC-dimensio
	Rob Schapiren boosting sivu L. Valiant: A theory of the learnable. Communications of the ACM 27 (1984) 1134-1142. A. Blumer, A. Ehrenfeucht, D. Haussler & M. Warmuth: Occam's Razor. Information Processing Letters 24 (1987) 377-380. A. Blumer, A. Ehrenfeucht, D. Haussler & M. Warmuth: Learnability and the Vapnik-Chervonenkis dimension. Journal of the ACM 36 (1989) 929-965.
15. 2.:	Pekka Isto: Boosting - Yleistä
	Y. Freund & R. Schapire: Experiments with a new boosting algorithm. In Proc. 13th ICML (pp. 148-156), Morgan Kaufmann, 1996. J. Kivinen & M. Warmuth: Boosting as entropy projection. In Proc. 12th COLT (pp. 134-144). ACM, New York, 1999. R. Schapire & Y. Singer: Improved boosting algorithms using confidence-rated predictions. Machine Learning 37 (1999) 297-336.
22. 2.:	Kimmo Takkunen: Boosting - Kokeita ja sovelluksia
	E. Bauer & R. Kohavi: An empirical comparison of voting classification algorithms: bagging, boosting, and variants. Machine Learning 36 (1999) 105-139. William W. Cohen and Yoram Singer: Simple, fast, and effective rule learner. AAAI-99. T. Dietterich: An experimental comparison of three methods for constructing ensembles of decision trees: bagging, boosting and randomization. Machine Learning, to appear. Ross Quinlan. Bagging, boosting, and C4.5. In Proc. 14th AAAI, 1996.
29. 2.:	Tibor Hegedus: Boosting - Teoriaa
	L. Mason, J. Baxter, P. Bartlett & M. Frean: Boosting algorithms as gradient descent in function space. Technical report. R. Schapire, Y. Freund, P. Bartlett & W. Lee: Boosting the margin: A new explanation for the effectiveness of voting methods. The Annals of Statistics, 26(5): 1651-1686, 1998.
7. 3.:	Teemu Kurppa: Palauteoppiminen
	L. Kaelbling, M. L. Littman & A. W. Moore: Reinforcement learning: a survey. Journal of Artificial Intelligence Research 4 (1996) 237-285.
14. 3.:	Tapio Tanskanen: Tekstin luokittelu
	D. Lewis, R. Schapire, J. Callan & R. Papka: Training algorithms for linear text classifiers. In Hans-Peter Frei,D onna Harman, Peter Schauble, and Ross Wilkinson, editors, SIGIR '96: Proc. 19th Ann. Int. ACM SIGIR Conf. on Research and Development in Information Retrieval (pp. 298-306). Konstanz, 1996. Hartung-Gorre Verlag. R. Schapire & Y. Singer. BoosTexter: A boosting-based system for text categorization. Machine Learning 39 (2000) 1-34.
21. 3.:	Tommy Nyqvist: Luokittelu leveällä marginaalilla
	Y. Freund & R. Schapire: Large margin classification using the perceptron algorithm. Machine Learning 37 (1999) 277-296. J. Shawe-Taylor, P. L. Bartlett, R. C. Williamson & M. Anthony: Structural risk minimization over data-dependent hierarchies. IEEE Transactions on Information Theory 44 (1998) 1926-1940. J. Shawe-Taylor & N. Cristianini: Further results on the margin distribution. In Proc. 12th COLT (pp. 278-285), ACM, 1999.
24. 3.:	Prof. Jouko Lampinen (Laskennallisen tekniikan lab., TKK): Bayesian Techniques for
	Neural Networks - Review and Case Studies
	Älykkäiden järjestelmien tutkimusseminaarissa perjantaina 24. 3. klo 10-12, B453
4. 4.:	Marko Salmenkivi: Päätöspuiden karsintamenetelmät Klo 13-15
	J. Mingers: An empirical comparison of pruning methods for decision tree induction. Machine Learning 3 (1989) 227-243. Oates, T. and Jensen, D.: Toward a theoretical understanding of why and when decision tree pruning algorithms fail. Proc. 16th AAAI. AAAI Press, 1999.
4. 4.:	Vierailuluento klo 15-17: Jorma Rissanen: Complexity and information in data
11. 4.:	Jussi Lindgren: Päätöspuiden karsintamenetelmät
	D. Helmbold & R. Schapire: Predicting nearly as well as the best pruning of a decision tree. Machine Learning 27 (1997) 51-68. M. Kearns & Y. Mansour: A fast, bottom-up decision tree pruning algorithm with near-optimal generalization. Proc. 15th ICML. Morgan Kaufmann, 1998. Y. Mansour: Pessimistic decision tree pruning based on tree size. Proc. 14th ICML (pp. 195-201). Morgan Kaufmann, 1997. F. Pereira & Y. Singer: An efficient extension to mixture techniques for prediction and decision trees. Proc. 10th COLT (pp. 114-121). ACM Press, 1997.
18. 4.:	Tuomas Lepola: Generoiva topografinen kuvaus (GTM) ja sen malliparametrien oppiminen EM-algoritmilla
	C. M. Bishop, M. Svensén & C. K. I. Williams: GTM: The Generative Topographic Mapping. Neural Computation 10 (1998) 215-234. C. M. Bishop, M. Svensén & C. K. I. Williams: EM Optimization of Latent-Variable Density Models. In D. S. Touretzky, M. C. Mozer & M. E. Hasselmo (eds.), NIPS 8 (pp. 465-471), MIT Press, 1996. K. Nigam, A. McCallum, S. Thrun & T. Mitchell: Text Classification from Labeled and Unlabeled Documents using EM. Machine Learning (2000) to appear.
2. 5.:	Ari Rantanen: Tukivektorikoneet (support vector machine, SVM)
	SVM-kotisivu A. Rantanen: Datan luokittelu tukivektorikoneilla B. Boser, I. Guyon & V. Vapnik: A training algorithm for optimal margin classifiers. In Proc. 5th COLT (pp. 144-152), ACM, 1992 Burges, C. J. C: A tutorial on support vector machines for pattern recognition. Data Mining and Knowledge Discovery 2 (1998)

AAAI = National Conference on Artificial Intelligence
COLT = Annual ACM Conference on Computational Learning Theory
ECML = European Conference on Machine Learning
ICML = International Conference on Machine Learning
NIPS = Advances in Neural Information Processing Systems

elomaa@cs.helsinki.fi