Fractionally-Supervised Classification
نویسندگان
چکیده
منابع مشابه
Detecting Concept Drift in Data Stream Using Semi-Supervised Classification
Data stream is a sequence of data generated from various information sources at a high speed and high volume. Classifying data streams faces the three challenges of unlimited length, online processing, and concept drift. In related research, to meet the challenge of unlimited stream length, commonly the stream is divided into fixed size windows or gradual forgetting is used. Concept drift refer...
متن کاملSupervised Learning for Classification
Supervised local tangent space alignment is proposed for data classification in this paper. It is an extension of local tangent space alignment, for short, LTSA, from unsupervised to supervised learning. Supervised LTSA is a supervised dimension reduction method. It make use of the class membership of each data to be trained in the case of multiple classes, to improve the quality of classificat...
متن کاملOn Semi-Supervised Classification
A graph-based prior is proposed for parametric semi-supervised classification. The prior utilizes both labelled and unlabelled data; it also integrates features from multiple views of a given sample (e.g., multiple sensors), thus implementing a Bayesian form of co-training. An EM algorithm for training the classifier automatically adjusts the tradeoff between the contributions of: (a) the label...
متن کاملEnsembles for Supervised Classification
This dissertation studies the use of multiple classi ers (ensembles or committees) in learning tasks. Both theoretical and practical aspects of combining classi ers are studied. We consider two di erent goals: The rst is to achieve better classi cation rates. We analyze both the representation ability of ensembles and algorithms that search for a solution in this representation space. Second, w...
متن کاملSemi-Supervised Classification with Universum
The Universum data, defined as a collection of ”nonexamples” that do not belong to any class of interest, have been shown to encode some prior knowledge by representing meaningful concepts in the same domain as the problem at hand. In this paper, we address a novel semi-supervised classification problem, called semi-supervised Universum, that can simultaneously utilize the labeled data, unlabel...
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 Classification
سال: 2015
ISSN: 0176-4268,1432-1343
DOI: 10.1007/s00357-015-9188-9