Efficient Cross-Domain Classification of Weblogs
نویسندگان
چکیده
منابع مشابه
Cross-domain classification using generalized domain acts
Cross-domain classi cation for speech understanding is an interesting research problem because of the need for portable solutions in the design for spoken dialogue systems. In this paper, a twotier classi er is proposed for speech understanding. The rst tier consists of domain independent dialogue acts while the second tier consists of application actions that are domain speci c. A maximum like...
متن کاملWeblogs Content Classification Tools: performance evaluation
Nowadays, weblogs or blogs are important tools for personal or workgroup websites publication. These tools give the necessary performances to create, edit, evaluate, publish and file digital contents, in the framework of a standarized workflow, and for managing the digital information life cycle. Nevertheless, these tools must be complemented with existence of technical funcionalities necessary...
متن کاملEfficient Cross-Domain Learning of Complex Skills
Building an intelligent agent that simulates human learning of math and science could potentially benefit both education, by contributing to the understanding of human learning, and artificial intelligence, by advancing the goal of creating human-level intelligence. However, constructing such a learning agent currently requires significant manual encoding of prior domain knowledge; in addition ...
متن کاملSample-oriented Domain Adaptation for Image Classification
Image processing is a method to perform some operations on an image, in order to get an enhanced image or to extract some useful information from it. The conventional image processing algorithms cannot perform well in scenarios where the training images (source domain) that are used to learn the model have a different distribution with test images (target domain). Also, many real world applicat...
متن کاملCross-domain Text Classification using Wikipedia
Traditional approaches to document classification requires labeled data in order to construct reliable and accurate classifiers. Unfortunately, labeled data are seldom available, and often too expensive to obtain, especially for large domains and fast evolving scenarios. Given a learning task for which training data are not available, abundant labeled data may exist for a different but related ...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: International Journal of Intelligent Computing Research
سال: 2010
ISSN: 2042-4655
DOI: 10.20533/ijicr.2042.4655.2010.0007