Semisupervised Learning Using the AdaBoost Algorithm with SVM-KNN
نویسندگان
چکیده
منابع مشابه
Semi-supervised Learning for SVM-KNN
Compared with labeled data, unlabeled data are significantly easier to obtain. Currently, classification of unlabeled data is an open issue. In this paper a novel SVMKNN classification methodology based on Semi-supervised learning is proposed, we consider the problem of using a large number of unlabeled data to boost performance of the classifier when only a small set of labeled examples is ava...
متن کاملAdaBoost with SVM-based component classifiers
The use of SVM (Support Vector Machine) as component classifier in AdaBoost may seem like going against the grain of the Boosting principle since SVM is not an easy classifier to train. Moreover, Wickramaratna et al. [2001. Performance degradation in boosting. In: Proceedings of the Second International Workshop on Multiple Classifier Systems, pp. 11–21] show that AdaBoost with strong component...
متن کاملThe kNN-TD Reinforcement Learning Algorithm
A reinforcement learning algorithm called kNN-TD is introduced. This algorithm has been developed using the classical formulation of temporal difference methods and a k-nearest neighbors scheme as its expectations memory. By means of this kind of memory the algorithm is able to generalize properly over continuous state spaces and also take benefits from collective action selection and learning ...
متن کاملA Hybrid Text Classification Approach Using KNN And SVM
Text classification is the process of assigning text documents based on certain categories. A classifier is used to define the appropriate class for each text document based on the input algorithm used for classification. Due to the emerging trends in the field of internet and computers ,billions of text data are processed at a given time and so there is a need for organizing these data to prov...
متن کاملLearning speaker normalization using semisupervised manifold alignment
As a child acquires language, he or she: perceives acoustic information in his or her surrounding environment; identifies portions of the ambient acoustic information as languagerelated; and associates that language-related information with his or her perception of his or her own language-related acoustic productions. The present work models the third task. We use a semisupervised alignment alg...
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 Transactions of The Korean Institute of Electrical Engineers
سال: 2012
ISSN: 1975-8359
DOI: 10.5370/kiee.2012.61.9.1336