Arvet efter Arn (Arn, Book 4) by Jan Guillou

By Jan Guillou

Ingrid Ylva blev en legend redan i sin livstid vid mitten av 1200-talet. Hon var en av flera starka änkor som styrde riket efter de blodiga segrarna mot Danmark. Om henne sades att hon var trollkunnig och att så länge hon höll sitt huvud högt skulle intet ont drabba folkungaätten.

Med järnvilja fostrade hon sina söner i en skoningslös tid där den starkares rätt var den enda lag som gällde. Att simply en av hennes söner until slut skulle segra i maktkampen kunde inte ha förvånat någon. males med en hänsynslöshet som until eventually och med skrämde hans samtid krossade Birger Magnusson maffiaväldet för all framtid. Och de lagar han stiftade om hemfrid, kyrkofrid och kvinnofrid blev rikets norm för six hundred år framåt.

Historien känner honom som Birger jarl, Stockholms grundare och Sveriges skapare. males sagan om Birger är mycket större än så.

Arn Magnusson må ha varit ett helgon. Det var sannerligen inte Birger. males han var en segrare som inte vek undan för moraliska eller praktiska prevent på sin väg från yngling i Västra Götaland until eventually riksbyggare.

Arvet efter Arn är en vacker och blodig riddarsaga där marken skälver lower than dundrande hästhovar och skummet year om korsfararnas fartygsbogar på väg österut. Det är också berättelsen om förlorad kärlek som en del av maktens höga pris.

Detta är en fristående fortsättning på Jan Guillous trilogi om riddare Arn Magnusson. Trilogin har på bara några år sålt i över 1 miljon exemplar i Sverige. Böckerna är below utgivning i de nordiska länderna samt i Holland, Tyskland, Italien, Frankrike, Spanien och England.

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On the other hand, the methods outlined in the previous section and section 5 can be used to handle gradients with negative components. The approach used by R¨ atsch et al. [13] can similarly be interpreted as a potential function of the margins. Recently, Friedman et al. [8] have given a maximum likelihood motivation for AdaBoost, and introduced another leveraging algorithm based on the log-likelihood criteria. They indicate that minimizing the square loss potential, (H − 1)2 performed less well in experiments than other monotone potentials, and conjecture that its non-monotonicity (penalizing margins greater than 1) is a contributing factor.

We define bad pair to be a pair of hypotheses from the version space that differ on more then proportion α of the samples. e. if two hypotheses are picked independently from the version space, the probability that they form a bad pair is less then β4k . We will show that if the algorithm did not make a query for tk consecutive samples, then the probability that the queries sampled do not form an α − β4k -net, is bounded by βk /4. Let W = {(h1 , h2 ) |Prx [h1 (x) = h2 (x)] ≥ α}. We would like to bound the probability that Pr[W ] > βk /4 when QBC” didn’t query for a tag for tk at the last consecutive samples: If Pr[W ] > βk /4, then the probability that the QBC algorithm will query for a tag is greater then αβk /4.

Learning Theory, pages 277–289, San Mateo, CA, 1991. Morgan Kaufmann. 2. B. E. Boser, I. M. Guyon, and V. N. Vapnik. A training algorithm for optimal margin classifiers. In Proc. 5th Annu. Workshop on Comput. Learning Theory, pages 144–152. ACM Press, New York, NY, 1992. 3. Leo Breiman. Bagging predictors. Machine Learning, 24(2):123–140, 1996. 4. Leo Breiman. Arcing the edge. Technical Report 486, Department of Statistics, University of California, Berkeley, 1997. edu. 5. Leo Breiman. Bias, variance, and arcing classifiers.

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