[PDF][PDF] The value of unlabeled data for classification problems
T Zhang, F Oles - … of the Seventeenth International Conference on …, 2000 - Citeseer
… rest 9583 training data points as unlabeled data to train a transductive version of SVM. The
… the transductive SVM evaluated on the unlabeled data is worse than that of the supervised …
… the transductive SVM evaluated on the unlabeled data is worse than that of the supervised …
Learning classification with both labeled and unlabeled data
… We have performed experiments using neural networks and support vector machines but
for the classification tasks considered here, they did not show any improvement over a simple …
for the classification tasks considered here, they did not show any improvement over a simple …
Unlabeled data classification via support vector machines and k-means clustering
L Maokuan, C Yusheng… - … Conference on Computer …, 2004 - ieeexplore.ieee.org
… based on support vector machines to classify unlabeled data more accurately after training
the data labelled by k-means clustering algorithms. For classifying unlabeled data, which is …
the data labelled by k-means clustering algorithms. For classifying unlabeled data, which is …
[PDF][PDF] Is unlabeled data suitable for multiclass SVM-based web page classification?
… of unlabeled data for multiclass SVM classification tasks. We … semisupervised SVM approach
to conduct this kind of tasks. … explain how SVM classifiers work for binary classifications, …
to conduct this kind of tasks. … explain how SVM classifiers work for binary classifications, …
A bagging SVM to learn from positive and unlabeled examples
F Mordelet, JP Vert - Pattern Recognition Letters, 2014 - Elsevier
… identifying a data of interest is difficult or expensive, eg, because human intervention is
necessary or expensive experiments are needed, while unlabeled data … Unlabeled data are data …
necessary or expensive experiments are needed, while unlabeled data … Unlabeled data are data …
Incremental support vector machine for unlabeled data classification
… Using a special characteristic of SVM-classifier, this paper proposes the algorithm that
improves the performance of text classification by incremental addition of unlabeled data to the …
improves the performance of text classification by incremental addition of unlabeled data to the …
Semi-superyised support vector machines for unlabeled data classification
G Fung, OL Mangasarian - Optimization methods and software, 2001 - Taylor & Francis
… classifying unlabeled data based on the following ideas: (i) A small representative percentage
(5% to 10%) of the unlabeled data … remaining bulk of the unlabeled dataset to one of two …
(5% to 10%) of the unlabeled data … remaining bulk of the unlabeled dataset to one of two …
[PDF][PDF] Learning to classify texts using positive and unlabeled data
… We use Roc-SVM and Roc-Clu-SVM to denote the classification techniques that employ
Rocchio and Rocchio with clustering to extract reliable negative set respectively (both methods …
Rocchio and Rocchio with clustering to extract reliable negative set respectively (both methods …
Labeled and unlabeled data in text categorization
… use of unlabeled data as a way to improve classification performance in text categorization.
The ready availability of this kind of data in most … Our objective is to compare, using a SVM …
The ready availability of this kind of data in most … Our objective is to compare, using a SVM …
Towards making unlabeled data never hurt
… [16] discussed the reason why unlabeled data can increase classification error for generative
meth… of the inductive SVM. Otherwise we will rely on the prediction of the inductive SVM. …
meth… of the inductive SVM. Otherwise we will rely on the prediction of the inductive SVM. …
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