Active learning with support vector machines
نویسندگان
چکیده
منابع مشابه
Active learning with support vector machines
In machine learning, active learning refers to algorithms that autonomously select the data points from which they will learn. There are many data mining applications in which large amounts of unlabeled data are readily available, but labels (e.g., human annotations or results from complex experiments) are costly to obtain. In such scenarios, an active learning algorithm aims at identifying dat...
متن کاملActive Learning with Support Vector Machines
This thesis examines the use of support vector machines for active learning using linear, polynomial and radial basis function kernels. In our experiments we used named entity recognition which was treated as a binary task and as a multiclass task and we also tackled shallow parsing. We report savings in annotation costs ranging from 80% to 95% depending on the task. We observed that the distri...
متن کاملOn Multiclass Active Learning with Support Vector Machines
In supervised machine learning, a training set of examples which are assigned to the correct target labels is a necessary prerequisite. However, in many applications, the task of assigning target labels cannot be conducted in an automatic manner, but involves human decisions and is therefore time-consuming and expensive. In the case of classification learning, the active learning framework has ...
متن کاملActive Learning for Support Vector Machines with Maximum Model Change
Margin-based strategies and model change based strategies represent two important types of strategies for active learning. While margin-based strategies have been dominant for Support Vector Machines (SVMs), most methods are based on heuristics and lack a solid theoretical support. In this paper, we propose an active learning strategy for SVMs based on Maximum Model Change (MMC). The model chan...
متن کاملActive Learning with Semi-Supervised Support Vector Machines
A significant problem in many machine learning tasks is that it is time consuming and costly to gather the necessary labeled data for training the learning algorithm to a reasonable level of performance. In reality, it is often the case that a small amount of labeled data is available and that more unlabeled data could be labeled on demand at a cost. If the labeled data is obtained by a process...
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: Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery
سال: 2014
ISSN: 1942-4787
DOI: 10.1002/widm.1132