Comparing machine learning approaches for context-aware composition

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 6708 LNCS, Issue , 2011, Pages 18-33

  Danylenko, Antonina ^a   Kessler, Christoph ^b   Löwe, Welf ^a  

^a LINNAEUS UNIVERSITY   (Sweden)

^b LINKÖPING UNIVERSITY   (Sweden)

Author keywords

Autotuning; Context Aware Composition; Machine Learning

Indexed keywords

AUTOTUNING; CONTEXT ATTRIBUTES; CONTEXT-AWARE; DECISION DIAGRAM; DYNAMIC DISPATCH; HARDWARE PLATFORM; MACHINE-LEARNING; MEMORY CONSUMPTION; NAIVE BAYES; OPTIMAL VARIANTS; PROGRAM POINTS; RUNTIMES;

DECISION MAKING; DECISION TREES; LEARNING SYSTEMS; OBJECT ORIENTED PROGRAMMING;

DECISION TABLES;

EID: 79960128332     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-22045-6_2     Document Type: Conference Paper

References (30)

1. Hirschfeld, R., Costanza, P., Nierstrasz, O.: Context-oriented programming. Journal of Obj. Tech. ETH Zurich 7(3), 125-151 (2008)
2. Costanza, P., Hirschfeld, R.: Language constructs for context-oriented programming: an overview of contextl. In: Proc. of the 2005 Symposium on Dynamic Lang, pp. 1-10. ACM, New York (2005)
3. Nilsson, N.J.: Introduction to machine learning: An early draft of proposed text book. Stanford University, Stanford (1996)
4. Han, J., Kamber, M.: Data Mining: Concepts and Techniques, 2nd edn. The Morgan Kaufmann Series in Data Management Systems. Morgan Kaufmann, San Francisco (2000)
5. Moshkov, M.: Algorithms for constructing of decision trees. In: Komorowski, J., Żytkow, J.M. (eds.) PKDD 1997. LNCS, vol. 1263, pp. 335-342. Springer, Heidelberg (1997)
6. Rokach, L., Maimon, O.: Data Mining with Decision Trees: Theory and Applications. World Scientific, Singapore (2008)
7. Bryant, R.E.: Symbolic boolean manipulation with ordered binary-decision diagrams. ACM Computing Surveys 24, 293-318 (1992)
8. Bryant, R.E.: Graph-based algorithms for boolean function manipulation. IEEE Transactions on Computers 35, 677-691 (1986)
9. Johnson, S.: Branching programs and binary decision diagrams: theory and applications by Ingo Wegener society for industrial and applied mathematics, vol. 41, pp. 36-38. ACM, New York (2010)
10. Mitchell, T.M.: Machine Learning. McGraw-Hill, New York (1997)
11. Keogh, E.J., Pazzani, M.J.: Learning augmented bayesian classifiers: A comparison of distribution-based and classification-based approaches (1999)
12. Cortes, C., Vapnik, V.: Support-vector networks. Mach. Learn. 20, 273-297 (1995)
13. Chang, C.-C., Lin, C.-J.: Libsvm - a library for support vector machines. National Taiwan University, Dep. of Comp. Science and Inf. Eng. (2001)
14. Hsu, C.-W., Chang, C.-C., Lin, C.-J.: A practical guide to support vector classification. National Taiwan University, Dep. of Comp. Science, Tech. Rep. (2003)
15. Kramer, K.A., Hall, L.O., Goldgof, D.B., Remsen, A., Luo, T.: Fast support vector machines for continuous data. Trans. Sys. Man Cyber. 39, 989-1001 (2009)
16. Cormen, T.H., Leiserson, C.E., Rivest, R.L., Stein, C.: Introduction to Algorithms. The MIT Press, New York (2001)
17. Quinlan, J.R.: C4.5: programs for machine learning. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA (1993)
18. Andersson, J., Ericsson, M., Kessler, C.W., Löwe, W.: Profile-guided composition. In: Pautasso, C., Tanter, É. (eds.) SC 2008. LNCS, vol. 4954, pp. 157-164. Springer, Heidelberg (2008)
19. von Löwis, M., Denker, M., Nierstrasz, O.: Context-oriented programming: beyond layers. In: Proc. of the 2007 Int. Conf. on Dynamic lang.: in conjunction with the 15th Int. Smalltalk Joint Conf. 2007, pp. 143-156. ACM, New York (2007)
20. Moura, J.M.F., Johnson, J., Johnson, R.W., Padua, D., Prasanna, V.K., Püschel, M., Singer, B., Veloso, M., Xiong, J.: Generating platform-adapted DSP libraries using SPIRAL. In: High Performance Embedded Computing, HPEC (2001)
21. Moura, J.M.F., Johnson, J., Johnson, R.W., Padua, D., Prasanna, V.K., Püschel, M., Veloso, M.: SPIRAL: Automatic implementation of signal processing algorithms. In: High Performance Embedded Computing, HPEC (2000)
22. Li, X., Garzarán, M.J., Padua, D.: A dynamically tuned sorting library. In: Proc. CGO 2004, Palo Alto, CA, USA, pp. 111-124 (2004)
23. Brewer, E.A.: High-level optimization via automated statistical modeling. In: PPoPP 1995 (1995)
24. Thomas, N., Tanase, G., Tkachyshyn, O., Perdue, J., Amato, N.M., Rauchwerger, L.: A framework for adaptive algorithm selection in STAPL. In: Proc. ACM SIGPLAN Symp. on Princ. and Pract. of Parallel Programming, pp. 277-288. ACM, New York (2005)
25. Yu, H., Rauchwerger, L.: An adaptive algorithm selection framework for reduction parallelization. IEEE Trans. Par. Distr. Syst. 17, 1084-1096 (2006)
26. Kessler, C., Löwe, W.: A framework for performance-aware composition of explicitly parallel components. In: Bischof, C., et al. (eds.) ParCo-2007: Jülich/Aachen Parallel Computing: Architectures, Algorithms and Applications. Advances in Parallel Computing Series, vol. 15, pp. 227-234. IOS Press, Amsterdam (2008)
27. Kessler, C., Löwe, W.: Optimized composition of performance-aware parallel components. In: 15th Workshop on Compilers for Parallel Computing CPC, July 7-9. University of Technology, Vienna (2010)
28. Olszewski, M., Voss, M.: An install-time system for the automatic generation of optimized parallel sorting algorithms. In: Proc. PDPTA 2004, vol. 1 (June 2004)
29. Ansel, J., Chan, C.P., Wong, Y.L., Olszewski, M., Zhao, Q., Edelman, A., Amarasinghe, S.P.: PetaBricks: a language and compiler for algorithmic choice. In: Proc. ACM SIGPLAN Conf. on Progr. Language Design and Implem, pp. 38-49. ACM, New York (2009)
30. Wernsing, J.R., Stitt, G.: Elastic computing: a framework for transparent, portable, and adaptive multi-core heterogeneous computing. In: Proc. ACM Conf. on Lang, compilers, and tools for embedded systems (LCTES 2010), pp. 115-124. ACM, New York (2010)