Get Machine Learning, Optimization, and Big Data: First PDF

By Panos Pardalos, Mario Pavone, Giovanni Maria Farinella, Vincenzo Cutello

ISBN-10: 3319279254

ISBN-13: 9783319279251

ISBN-10: 3319279262

ISBN-13: 9783319279268

This booklet constitutes revised chosen papers from the 1st overseas Workshop on desktop studying, Optimization, and massive information, MOD 2015, held in Taormina, Sicily, Italy, in July 2015.
The 32 papers offered during this quantity have been rigorously reviewed and chosen from seventy three submissions. They care for the algorithms, tools and theories appropriate in facts technology, optimization and desktop studying.

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Read Online or Download Machine Learning, Optimization, and Big Data: First International Workshop, MOD 2015, Taormina, Sicily, Italy, July 21-23, 2015, Revised Selected Papers PDF

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Additional info for Machine Learning, Optimization, and Big Data: First International Workshop, MOD 2015, Taormina, Sicily, Italy, July 21-23, 2015, Revised Selected Papers

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Pseudo-code of the Sensitive Algorithmic Tuning (SAT) algorithm. 1: procedure SAT(A, X− , X + ) 2: k←0 3: Mk ← Morris(A, X− , X + , r, p, Δ) 4: while ¬StopCondition do 5: si ← max(Mk ) 6: [lx− , lx+ ] ←LowSplit(X− , X + , si ) 7: [hx− , hx+ ] ←HighSplit(X− , X + , si ) 8: Mlx ←Morris(A, lx− , lx+ , r, p, Δ) 9: Mhx ←Morris(A, hx− , hx+ , r, p, Δ) 10: if max f (Mlx ) > max f (Mhx ) then 11: [X− , X + ] ← [lx− , lx+ ] 12: Mk ← Mlx 13: else 14: [X− , X + ] ← [hx− , hx+ ] 15: Mk ← Mhx 16: end if 17: k ←k+1 18: end while 19: end procedure Automatic Tuning of Algorithms Through Sensitivity Minimization 19 Morris {[1, 10], [1, 10]} Morris Morris {[1, 5], [1, 10]} {[5, 10], [1, 10]} Morris ∅ Morris {[5, 10], [1, 5]} {[5, 10], [5, 10]} ∅ Fig.

N . Individual Hypotheses: Formulated problem is equivalent to multiple (simultaneous) testing of individual hypothesis hi : Shi ≤ Sh0 vs ki : Shi > Sh0 i = 1, . . , N. ,iN is true. ,jN ) = 1, ∃k such that ik = 1 and jk = 0 0, else (4) The loss functions W1 and W2 are traditionally used in the theory of multiple hypotheses testing [6,13]. Another type of loss functions (so called additive loss functions) was introduced in [12] and used in multiple decision theory and applications [8,9,11]. ,jN such that js = 1 if is = 1) Note that Risk(W1 ) is equal to the probability of at least one false rejection (type I error), and Risk(W2 ) is equal to the probability of at least one false acceptance (type II error).

J. Uncertain Syst. 4(1), 14–21 (2010) 22. : The pagerank citation ranking: Bringing order to the web. P. A. M. ru Abstract. Stock selection by Sharp ratio is considered in the framework of multiple statistical hypotheses testing theory. The main attention is paid to comparison of Holm step down and Hochberg step up procedures for different loss functions. Comparison is made on the basis of conditional risk as a function of selection threshold. This approach allows to discover that properties of procedures depend not only on relationship between test statistics, but also depend on dispersion of Sharp ratios.

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Machine Learning, Optimization, and Big Data: First International Workshop, MOD 2015, Taormina, Sicily, Italy, July 21-23, 2015, Revised Selected Papers by Panos Pardalos, Mario Pavone, Giovanni Maria Farinella, Vincenzo Cutello


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