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.
Writing and browsing education blogs has become one of the important methods of e-learning. Learners can search the interesting resources from these education blogs. However, the traditional blog search only provides keyword-based matching, lacking automatic extraction of learner interests and further interest-related
This paper presents a novel method to extract Protein-Protein Interaction (PPI) information from biomedical literatures based on Support Vector Machine (SVM) and K Nearest Neighbors (KNN). The two protein names, words between two proteins, words surrounding two proteins, keyword between or among the surrounding words
This article proposes such a question classification approach that integrates multiple semantic features. It is aimed at these two questions in Chinese question classification models: inaccurate semantic information extraction and too slow processing speed caused by too high Eigenvector dimension. With the help of HowNet and the support vector machine and syntactic and semantic information of question...
Search By Multiple Examples (SBME) is a new search paradigm that allows users to specify their information needs as a set of relevant documents rather than as a set of keywords. In this study, we propose a Transductive Positive Unlabeled learning (TPU learning) based framework for SBME. The framework consists of two
to describe a document instead of traditional keywords vector, which is based on merging words with high similarity and using a concept to describe the semantic feature rather than a series of words. It not only reduces feature dimension but also adds semantic information to the vector. We also use sentence (document
Traditional information retrieval (IR) method use keywords matching to filter the documents, but usually retrieves unrelated Web pages. In order to effectively classify Web pages, we present a Web page categorization algorithm, named WebPSC (Web page similarity categorization). This algorithm uses latent semantic
obtain latent semantic structure of original term-document matrix solving the polysemous and synonymous keywords problem. LS-SVM is an effective method for learning the classification knowledge from massive data, especially on condition of high cost in getting labeled classical examples. We adopt a novel method of Web page
In order to enable more effective image retrieval via keywords, automatic image annotation and categorization becomes an important problem in computer vision and content based image retrieval. Unfortunately, there exists a semantic gap between the low-level feature vectors and the high-level semantics or concepts. In
Podaj zakres dat dla filtrowania wyświetlonych wyników. Możesz podać datę początkową, końcową lub obie daty. Daty możesz wpisać ręcznie lub wybrać za pomocą kalendarza.