Exploiting kernel-based feature weighting and instance clustering to transfer knowledge across domains
نویسندگان
چکیده
منابع مشابه
Exploiting kernel-based feature weighting and instance clustering to transfer knowledge across domains
Learning invariant features across domains is of vital importance to unsupervised domain adaptation, where classifiers trained on the training examples (source domain) need to adapt to a different set of test examples (target domain) in which no labeled examples are available. In this paper, we propose a novel approach to find the invariant features in the original space and transfer the knowle...
متن کاملFeature Weighting Method Based On Instance Correlation Using Discretization
In Machine Learning Process, several issues arise in identifying a suitable and quality set of features from which a classification model for a particular domain to be constructed. This paper addresses the problem of feature selection for machine learning through discretization approach. RELIEF is considered to be one of the most successful algorithms for assessing the quality of features. RELI...
متن کاملMercer kernel-based clustering in feature space
The article presents a method for both the unsupervised partitioning of a sample of data and the estimation of the possible number of inherent clusters which generate the data. This work exploits the notion that performing a nonlinear data transformation into some high dimensional feature space increases the probability of the linear separability of the patterns within the transformed space and...
متن کاملFeature Weighting and Instance Selection for Collaborative Filtering
Collaborative filtering uses a database about consumers’ preferences to make personal product recommendations and is achieving widespread success in E-Commerce nowadays. In this paper, we present several feature-weighting methods to improve the accuracy of collaborative filtering algorithms. Furthermore, we propose to reduce the training data set by selecting only highly relevant instances. We ...
متن کاملA Co-evolutionary Framework for Nearest Neighbor Enhancement: Combining Instance and Feature Weighting with Instance Selection
The nearest neighbor rule is one of the most representative methods in data mining. In recent years, a great amount of proposals have arisen for improving its performance. Among them, instance selection is highlighted due to its capabilities for improving the accuracy of the classifier and its efficiency simultaneously, by editing noise and reducing considerably the size of the training set. It...
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: TURKISH JOURNAL OF ELECTRICAL ENGINEERING & COMPUTER SCIENCES
سال: 2017
ISSN: 1300-0632,1303-6203
DOI: 10.3906/elk-1503-245