Download Machine Learning: ECML 2000: 11th European Conference on by Pedro Domingos (auth.), Ramon López de Mántaras, Enric Plaza PDF

By Pedro Domingos (auth.), Ramon López de Mántaras, Enric Plaza (eds.)

The biennial eu convention on laptop studying (ECML) sequence is meant to supply a world discussion board for the dialogue of the most recent top of the range examine ends up in desktop studying and is the main eu scienti?c occasion within the ?eld. The 11th convention (ECML 2000) held in Barcelona, Catalonia, Spain from may well 31 to June 2, 2000, has endured this custom through attracting top of the range papers from worldwide. Scientists from 21 international locations submitted a hundred papers to ECML 2000, from which 20 have been chosen for lengthy oral shows and 23 for brief oral shows. this option was once in response to the options of a minimum of reviewers for every submitted paper. it really is worthy noticing that the variety of papers reporting functions of computer studying has elevated compared to prior ECML meetings. We think this truth exhibits the growing to be adulthood of the ?eld. This quantity comprises the forty three permitted papers in addition to the invited talks via Katharina Morik from the collage of Dortmund and Pedro Domingos from the collage of Washington at Seattle. furthermore, 3 workshops have been together prepared by way of ECML 2000 and the ecu community of Excellence - web: “Dealing with established info in desktop studying and facts W- stites”, “Machine studying within the New info Age” , and “Meta-Learning: construction automated suggestion recommendations for version choice and procedure Com- nation”.

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Additional info for Machine Learning: ECML 2000: 11th European Conference on Machine Learning Barcelona, Catalonia, Spain, May 31 – June 2, 2000 Proceedings

Example text

30) i=1 This formulation of multi-class classification problem is more fault-tolerant than the 1-v -r general method, because there exist more redundancy in the answers [7]. On the other hand, all the K-SVCRs answers have sense: each machine classifies any input into a class, the two class implicated in the binary classification or into the ’rest’ class (0-label class). The 1-v -1 general classification method is more fault-tolerant that the 1-v -r one, but the classifiers give no sense answers if the evaluated input does not belong to the classes implicated in the binary classification.

CT =  +1 , . . , γ + 3 −1 −1 ,···, , δ, . . , δ y1 y 12 )∈R ∈R 12 + 3 + 3 (26) 12 + 3 + 3  (k (xi , xj )) − (k (xi , xj )) (k (xi , xj )) H =  − (k (xi , xj )) (k (xi , xj )) − (k (xi , xj ))  = HT ∈ S R (k (xi , xj )) − (k (xi , xj )) (k (xi , xj )) + 3 , subject to γi · yi ≥ 0, γi ≥ 0, i = 1, . . , 12 i = 12 , . . , + + 12 i=1 i= 3 3 γi − γi = (27) γi . i= +1 12 +1 The hyperplane decision function can be written as SV f (x) = +1, if νi k (xi , x) + b > δ (28) i=1 SV = −1, if νi k (xi , x) + b < δ i=1 = 0, otherwise νi = γi , i = 1, .

A break-out above from a minimum-band partially overlapping the previous max-band. The max-band is registered (as a positive example for SELL), but the current min-band is not, because of potential overlaps with the next band (not shown) Since successive extrema-band price ranges can overlap (leading to potential BUY operations at prices higher than some adjacent SELL), we need to eliminate such overlaps before registering the corresponding extrema bands as positive examples. We also need to delay the registration process of an extremum band B1 until the next extremum band B2 is computed, because B1's overlap with B2 cannot be computed before computing B2 (see Figure 2).

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