Information Measures and some Distribution Approximations.

The Fisher and Kullback- Liebler information measures were calculated from the approximation of a binomial distribution by both the Poisson and the normal distributions and are applied to the approximation of a Poisson distribution by a normal distribution. In this paper the concept of relative loss in information due to approximating the distribution of a random variable by that of another distribution of is introduced, and this concept is used to determine the value of the sample size for which the relative loss in information measure is less than a given level

Prevalence of Disorders Recorded in Dogs Attending Primary-Care Veterinary Practices in England

Purebred dog health is thought to be compromised by an increasing occurence of inherited diseases but inadequate prevalence data on common disorders have hampered efforts to prioritise health reforms. Analysis of primary veterinary practice clinical data has been proposed for reliable estimation of disorder prevalence in dogs. Electronic patient record (EPR) data were collected on 148,741 dogs attending 93 clinics across central and south-eastern England. Analysis in detail of a random sample of EPRs relating to 3,884 dogs from 89 clinics identified the most frequently recorded disorders as otitis externa (prevalence 10.2%, 95% CI: 9.1-11.3), periodontal disease (9.3%, 95% CI: 8.3-10.3) and anal sac impaction (7.1%, 95% CI: 6.1-8.1). Using syndromic classification, the most prevalent body location affected was the head-and-neck (32.8%, 95% CI: 30.7-34.9), the most prevalent organ system affected was the integument (36.3%, 95% CI: 33.9-38.6) and the most prevalent pathophysiologic process diagnosed was inflammation (32.1%, 95% CI: 29.8-34.3). Among the twenty most-frequently recorded disorders, purebred dogs had a significantly higher prevalence compared with crossbreds for three: otitis externa (P = 0.001), obesity (P = 0.006) and skin mass lesion (P = 0.033), and popular breeds differed significantly from each other in their prevalence for five: periodontal disease (P = 0.002), overgrown nails (P = 0.004), degenerative joint disease (P = 0.005), obesity (P = 0.001) and lipoma (P = 0.003). These results fill a crucial data gap in disorder prevalence information and assist with disorder prioritisation. The results suggest that, for maximal impact, breeding reforms should target commonly-diagnosed complex disorders that are amenable to genetic improvement and should place special focus on at-risk breeds. Future studies evaluating disorder severity and duration will augment the usefulness of the disorder prevalence information reported herein

EFFICIENCY OF LEARNING ALGORITHMS ON BIG DATA

Tematika te diplomske naloge so veliki podatki, karakteristike velikih podatkov in učni algoritmi, ki jih uporabljamo za klasifikacijo. V diplomski nalogi predstavljam tudi rezultate eksperimenta, s katerim sem ugotavljal učinkovitost učnih algoritmov na velike podatke. Učinkovitost algoritmov sem ovrednotil s klasifikacijsko točnostjo in časovnim izvajanjem učnih algoritmov na podatkovnih množicah. Iz pridobljenih rezultatov lahko sklepam, da se algoritmi glede na dane podatkovne množice različno obnašajo ter da je izbira učnega algoritma za analizo podatkovnih množic odvisna predvsem od problema in zastavljenega cilja.This thesis presents the subject of big data and its characteristics, learning algorithms applied in classification, as well as the results of an applied experiment in order to determine learning algorithms efficiency on big data. Algorithm efficiency has been assessed by classification accuracy and timely implementation of learning algorithms on datasets. The results indicate that algorithms perform differently considering given dataset and that preference of a learning algorithm intended for dataset analysis depends upon the posed problem and objective