2 resultados para Popularity.
em Memorial University Research Repository
Resumo:
The growing popularity of dog parks has created an opportunity to learn more about interactions between companion dogs. Dog-dog behaviour in a public off-leash dog park was described and analyzed using a motivationally-neutral approach. I observed focal dogs from park entry for 400 s and constructed activity time budgets (percentages of time spent with dogs, humans, etc.); rates of socially-relevant dog behaviours (e.g., snout-muzzle contact, physical contact) were also calculated. On average, focal dogs spent 50% of their time alone, nearly 40% with other dogs and 11% in other activities; time with dogs decreased and time alone increased over the first six minutes. Some behaviours were very frequent (i.e., more than 90% of focal dogs initiated and received snout-muzzle contact to the anogenital and head areas, while others were rare (i.e., 9% and 12% of focal dogs initiated and received lunge approaches, respectively). Dog density and focal dog age, sex, neuter status, and size were found to influence some behavioural variables. Future studies should continue to investigate the diverse range of canid behaviours and factors that influence social behaviours in dog park settings.
Resumo:
The social media classification problems draw more and more attention in the past few years. With the rapid development of Internet and the popularity of computers, there is astronomical amount of information in the social network (social media platforms). The datasets are generally large scale and are often corrupted by noise. The presence of noise in training set has strong impact on the performance of supervised learning (classification) techniques. A budget-driven One-class SVM approach is presented in this thesis that is suitable for large scale social media data classification. Our approach is based on an existing online One-class SVM learning algorithm, referred as STOCS (Self-Tuning One-Class SVM) algorithm. To justify our choice, we first analyze the noise-resilient ability of STOCS using synthetic data. The experiments suggest that STOCS is more robust against label noise than several other existing approaches. Next, to handle big data classification problem for social media data, we introduce several budget driven features, which allow the algorithm to be trained within limited time and under limited memory requirement. Besides, the resulting algorithm can be easily adapted to changes in dynamic data with minimal computational cost. Compared with two state-of-the-art approaches, Lib-Linear and kNN, our approach is shown to be competitive with lower requirements of memory and time.