Extended stochastic complexity and minimax relative loss analysis

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

Volumn 1720, Issue , 1999, Pages 26-38

  Yamanishi, Kenji ^a  

^a NEC CORPORATION   (Japan)

Author keywords

[No Author keywords available]

Indexed keywords

C (PROGRAMMING LANGUAGE); FORECASTING;

DECISION-THEORETIC; EFFECTIVE PERFORMANCE; LOSS FUNCTIONS; PREDICTION ALGORITHMS; REGRESSION PROBLEM; SEQUENTIAL PREDICTION; STOCHASTIC COMPLEXITY; UPPER AND LOWER BOUNDS;

STOCHASTIC SYSTEMS;

EID: 0009012367     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/3-540-46769-6_3     Document Type: Conference Paper

References (13)

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12. Yamanishi, K., A decision-theoretic extension of stochastic complexity and its applications to learning, IEEE Tran. on Inf. Theory, IT-44, 1424-1439(1998).
13. Yamanishi, K., Minimax relative loss analysis for sequential prediction algorithms using parametric hypotheses, in Proc. of COLT'98, ACM Press, pp:32-43(1998).