's Актуальные проблемы теории информации PDF

ISBN-10: 081764752X

ISBN-13: 9780817647520

ISBN-10: 0817647538

ISBN-13: 9780817647537

;Вопросы кибернетики. Выпуск 29. Актуальные проблемы теории информации КНИГИ ;НАУКА и УЧЕБА Название: Вопросы кибернетики. Выпуск 29. Актуальные проблемы теории информации Автор: Сборник. См. содержание Издательство: АН СССР Год издания: 1977 Страниц: one hundred forty Формат: DJVU Размер: 1,84 МБ Качество: Хорошее, six hundred дпи, цветные обложки.Сборник содержит работы участников семинара «Кодирование и передача информации в вычислительных сетях» при Научном совете по комплексной проблеме «Кибернетика» АН СССР. Сборник может быть полезен как специалистам по теории кодирования, так и всем лицам, связанным с разработкой систем обработки и nepeдачи информации.СОДЕРЖАНИЕЛ. Блох, В. В. Зяблов. Обобщенные каскадные кодыИ. М. Бояринов, Г. А. Кабатянский. Один класс арифметических итеративных кодовВ. Д. Колесник, Г. Ш. Полтырев. О сложности декодированияв дискретных постоянных каналахВ. Д. Колесшж, Г. Ш. Полтырев. О сложности декодирования итерационных кодовЛ. Е. Мазур. Класс кодов с мажоритарным декодированиемО. В. Попов, Л. Ф. Жигулин. Об анализе систем передачи дискретных сообщенийс переспросом eighty five

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We can discover that NLDA will save much computational cost under the most suitable situation it is applicable to. 4 Null Space-Based Kernel Fisher Discriminant Analysis The key idea of Kernel Fisher Discriminant Analysis (KFDA) [8, 9, 10] is to solve the problem of LDA in an implicit feature space F, which is constructed by the kernel trick: The important feature of kernel techniques is that the implicit feature vector needn’t be computed explicitly, while the inner product of any two vectors in F need to be computed based a kernel function.

Rodriguez From these results, it can be shown that the performance of AdaPix increase when increasing the number of classifiers. It can be shown also that they can be compared to the state-of-the-art (NC). AdaPix outperforms AdaHaar7 with less classifiers. Furthermore, AdaHaar7 obtained results far from the state-ofthe-art. As a fair comparison, we used our AdaBoost algorithm to boost weak classifiers for the three first types (Fig. 1) of Haar-like features (AdaHaar3), and we obtained an HTER two times smaller than AdaHaar7 with two times less classifiers.

We performed recognition by using both all the 9 basis images and the first 4 basis images. We report our experimental results and comparison to [23] in Table 1. From the experimental results, we find that our method gives good recognition rates. When the poses of training and testing images are very different, our method is not as good as [23] because we only used a set of feature points to recover the shape information and the shape recovery is not accurate enough. Pose Invariant Face Recognition Under Arbitrary Unknown Lighting 19 Fig.

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Актуальные проблемы теории информации


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