classification supervised
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Enhancement of TCP Over Wired/Wireless Networks With Packet Loss Classifiers Inferred by Supervised Learning
White Paper To build this classification algorithm, a database of pre-classified losses is gathered by simulating random network conditions, and classification models are automatically built from this database by using supervised learning methods. [03 Jul 2008]
Combining Labeled and Unlabeled Data for Text Classification With a Large Number of Categories
White Paper A major difficulty with supervised learning techniques for text classification is that they often require a large number of labeled examples to learn accurately. This paper proposes a new algorithm aimed at combining the advantages that ECOC offers... [03 Jul 2008]
Combining Labeled and Unlabeled Data for MultiClass Text Categorization
White Paper Supervised learning techniques for text classification often require a large number of labeled examples to learn accurately. Current text learning techniques for combining labeled and unlabeled, such as EM and Co-Training are mostly applicable for... [03 Jul 2008]
MMIHMM: Maximum Mutual Information Hidden Markov Models
White Paper Finally we illustrate the superiority of our approach in different classification tasks by comparing the classification performance of our proposed Maximum Mutual Information HMMs (MMIHMMs) with standard Maximum Likelihood HMMs (HMMs), in the case... [10 Apr 2008]
Offline/Realtime Traffic Classification Using Semi-Supervised Learning
White Paper This paper explores this latter approach and proposes a semi-supervised classification method that can accommodate both known and unknown applications. To the best of one's knowledge, this is the first work to use semi-supervised learning... [15 Feb 2008]
Semi-Supervised Learning of Mixture Models
White Paper This paper analyzes the performance of semi-supervised learning of mixture models. The authors show that unlabeled data can lead to an increase in classification error even in situations where additional labeled data would decrease classification... [07 Nov 2007]
Large Margin Non-Linear Embedding
White Paper This paper presents results for clustering and semi-supervised classification. The authors achieve this by combining an entropy-based embedding method with an entropy-based version of semi-supervised logistic regression. [19 Jul 2007]
Document Categorization in an XML
White Paper It is a supervised machine learning system that classifies documents such as news into predefined categories and tags them accordingly before storing them in an XML database. For classification, the k-NN and centroid-based algorithms are implemented. [25 Feb 2004]
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