Best-classifier feedback in diagnostic classification training
نویسندگان
چکیده
منابع مشابه
Document Classification as an Internet service: Choosing the best classifier
This project investigates some of the issues involved in a new proposal for expanding the scope of the field of Data Mining by providing mining models as services on the Internet. This idea can widely increase the reach and accessibility of Data Mining to common people because one of the primary stumbling blocks in the adoption of mining is the extremely high level of expertise and data resourc...
متن کاملTraining a Genre Classifier for Automatic Classification of Web Pages
This paper presents experimentson classifyingweb pages by genre. Firstly, a corpus of 1 539 manually labeled web pages was prepared. Secondly, 502 genre features were selected based on the literature and the observation of the corpus. Thirdly, these features were extracted from the corpus to obtain a data set. Finally, two machine learning algorithms, one for induction of decision trees (J48) a...
متن کاملEffective Sequential Classifier Training for Multitemporal Remote Sensing Image Classification
The explosive availability of remote sensing images has challenged supervised classification algorithms such as Support Vector Machines (SVM), as training samples tend to be highly limited due to the expensive and laborious task of ground truthing. The temporal correlation and spectral similarity between multitemporal images have opened up an opportunity to alleviate this problem. In this study...
متن کاملAttributes for Classifier Feedback
Traditional active learning allows a (machine) learner to query the (human) teacher for labels on examples it finds confusing. The teacher then provides a label for only that instance. This is quite restrictive. In this paper, we propose a learning paradigm in which the learner communicates its belief (i.e. predicted label) about the actively chosen example to the teacher. The teacher then conf...
متن کاملOptimally Training a Cascade Classifier
Cascade classifiers are widely used in real-time object detection. Different from conventional classifiers that are designed for a low overall classification error rate, a classifier in each node of the cascade is required to achieve an extremely high detection rate and moderate false positive rate. Although there are a few reported methods addressing this requirement in the context of object d...
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: Journal of Applied Research in Memory and Cognition
سال: 2015
ISSN: 2211-3681
DOI: 10.1016/j.jarmac.2015.07.007