Serwis Infona wykorzystuje pliki cookies (ciasteczka). Są to wartości tekstowe, zapamiętywane przez przeglądarkę na urządzeniu użytkownika. Nasz serwis ma dostęp do tych wartości oraz wykorzystuje je do zapamiętania danych dotyczących użytkownika, takich jak np. ustawienia (typu widok ekranu, wybór języka interfejsu), zapamiętanie zalogowania. Korzystanie z serwisu Infona oznacza zgodę na zapis informacji i ich wykorzystanie dla celów korzytania z serwisu. Więcej informacji można znaleźć w Polityce prywatności oraz Regulaminie serwisu. Zamknięcie tego okienka potwierdza zapoznanie się z informacją o plikach cookies, akceptację polityki prywatności i regulaminu oraz sposobu wykorzystywania plików cookies w serwisie. Możesz zmienić ustawienia obsługi cookies w swojej przeglądarce.
Most AdaBoost algorithms for multi-class problems have to decompose the multi-class classification into multiple binary problems, like the Adaboost.MH and the LogitBoost. This paper proposes a new multi-class AdaBoost algorithm based on hypothesis margin, called AdaBoost.HM, which directly combines multi-class weak classifiers. The hypothesis margin maximizes the output about the positive class meanwhile...
Tracking is recently formulated as a problem of discriminating the object from its nearby background, where the classifier is updated by new samples successively arriving during tracking. Depending on whether labeling the samples or not, the tracker can be designed in a supervised or semi-supervised manner. This paper proposes a novel semi-supervised algorithm for tracking by combining Semi-supervised...
This paper proposes a new tracking algorithm which combines object and background information, via building object and background appearance models simultaneously by non-parametric kernel density estimation. The major contribution is a novel bidirectional learning framework for discrimination between the object and background. It has the following advantages: 1) it embeds background information, unlike...
In many areas of pattern recognition and machine learning, low dimensional data are often embedded in a high dimensional space. There have been many dimensionality reduction and manifold learning methods to discover the low dimensional representation from high dimensional data. Locality based manifold learning methods often rely on a distance metric between neighboring points. In this paper, we propose...
The classification performance of nearest prototype classifiers largely relies on the prototype learning algorithms, such as the learning vector quantization (LVQ) and the minimum classification error (MCE). This paper proposes a new prototype learning algorithm based on the minimization of a conditional log-likelihood loss (CLL), called log-likelihood of margin (LOGM). A regularization term is added...
The discriminative learning of Bayesian networks benefits the classification accuracy as compared to generative learning. Previous approaches mostly learn either the structure or the parameters in a discriminative manner based on the scoring+ search paradigm. Many works have focused on structure learning by optimizing a discriminative scoring function but the resulted structure is still generative...
Podaj zakres dat dla filtrowania wyświetlonych wyników. Możesz podać datę początkową, końcową lub obie daty. Daty możesz wpisać ręcznie lub wybrać za pomocą kalendarza.