Research on Task-focused Massive Multi-source Heterogeneous Information Sharing & Utilizing Method
نویسندگان
چکیده
منابع مشابه
Exploiting High-Order Information in Heterogeneous Multi-Task Feature Learning
Multi-task feature learning (MTFL) aims to improve the generalization performance of multiple related learning tasks by sharing features between them. It has been successfully applied to many pattern recognition and biometric prediction problems. Most of current MTFL methods assume that different tasks exploit the same feature representation, and thus are not applicable to the scenarios where d...
متن کاملMulti-Robot Task Allocation Method for Heterogeneous Tasks with Priorities
Task allocation is a complex and open problem for multi-robot systems and very especially if a priority is associated to each task. In this paper, we present a method to allocate tasks with priorities in a team of heterogeneous robots. The system is partially inspired on auction and thresholds-based methods and tries to determine the optimum number of robots that are needed to solve specific ta...
متن کاملEpistemological Perspectives on Multi-Method Information Systems Research
There is a continuing discussion on methodological pluralism in IS research. Several claims have been made both for and against methodological pluralism. The debate focuses mainly on discussing the relationship between research methods and IS research paradigms, especially positivism and interpretivism. Also, the literature analyzes the epistemological assumptions of research paradigms, but pay...
متن کاملResearch on Collaborative Information Sharing Systems
Collaborative systems are systems designed to help people involved in a common task achieve their goals. They are widely used today, and they're gaining a great consensus both inside corporations and on the World Wide Web. There are many kinds of collaborative systems, such as Wikis (like Wikipedia), blogs, tag-based systems (like Flickr, del.icio.us and Bibsonomy) and even collabora-tive maps ...
متن کاملFocused Multi-task Learning Using Gaussian Processes
Given a learning task for a data set, learning it together with related tasks (data sets) can improve performance. Gaussian process models have been applied to such multi-task learning scenarios, based on joint priors for functions underlying the tasks. In previous Gaussian process approaches, all tasks have been assumed to be of equal importance, whereas in transfer learning the goal is asymme...
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: The International Conference on Electrical Engineering
سال: 2016
ISSN: 2636-4441
DOI: 10.21608/iceeng.2016.30304