Semi-Supervised Deep Rule-Based Classifier
Actively Semi-Supervised Deep Rule-based Classifier Applied to Adverse Driving Scenarios
... Actively Semi-Supervised Deep Rule-based Classifier Applied to Adverse Driving Scenarios Eduardo Soares, Plamen Angelov, Bruno Costa, Marcos Castro Abstract—This paper presents ...
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A Semi Supervised Deep Rule Based Approach for Remote Sensing Scene Classication
... is based on the recently introduced semi-supervised deep rule-based classifier for remote sensing scene ...pre-trained deep convoluational neural network as the ...
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Semi supervised deep rule based approach for image classification
... SSDRB classifier with different number of labelled images ...SSDRB classifier In the following numerical example, we investigate the influence of on the active learning mechanism and the performance of ...
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Benchmarking the Semi-Supervised Naïve Bayes Classifier
... data based on the UCI data. We do this by fitting a na¨ıve Bayes classifier to the original data, then using the model attribute distribution estimates to generate simulated ...Bayes classifier. ...
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Deep rule-based classifier with human-level performance and characteristics
... Indeed, DCNN is a powerful technique that provides high classification rates. There are also recently introduced approaches exploiting deep models for image understanding [31], [32] by learning informative hidden ...
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Deep rule based classifier with human level performance and characteristics
... Indeed, DCNN is a powerful technique that provides high classification rates. There are also recently introduced approaches exploiting deep models for image understanding [31], [32] by learning informative hidden ...
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Matrix Completion for Graph-Based Deep Semi-Supervised Learning
... two deep networks. Ras- mus et al. (Rasmus et al. 2015) merge supervised with unsu- pervised learning methods using a deep learning ...of supervised and un- supervised cost functions by ...
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Semi-supervised Deep Learning with Memory
... is based on the assumption that deep feature embeddings of each class can be gradually learned to distribute around its cluster centroid in the feature space ...[33]. Based on this assumption, the ...
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Towards deep semi supervised learning
... The success of many Machine Learning algorithms depends on data represen- tation. For example, a feature representation that successfully separates distinct classes can lead to perfect learning via simply a linear ...
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Multi-Train: A Semi-supervised Heterogeneous Ensemble Classifier
... Multi-Train, classifier diversity is gleaned by simultaneously manipulating data, manipulating input attributes, using vari- ous machine learning algorithms, and various ...voting rule; as a result, ...
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Multi-objective and semi-supervised heterogeneous classifier ensembles.
... The third contribution is to use one of the recent PSO variants, namely, compet- itive swarm optimiser (CSO), to select a small feature subset from the original large amount of attributes in the dataset. As the original ...
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SeNTU: Sentiment Analysis of Tweets by Combining a Rule based Classifier with Supervised Learning
... a rule- based classification ...a rule-based clas- sification layer can indeed refine the SVM’s predic- ...rules based on complex ...
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A cascade of deep learning fuzzy rule based image classifier and SVM
... fast, deep learning ensemble classifier is proposed and applied to the well-known benchmark problem of handwriting digits ...DLFRB classifier and a SVM based conflict resolution ...
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Deep Learning via Semi-Supervised Embedding
... – Compared to a reconstruction based loss function, such as used in an autoen- coder, our approach can be much cheaper to do the gradient updates. In our approach there is an encoding step, but no decoding step. ...
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Semi supervised Learning of Naive Bayes Classifier with feature constraints
... 6.2 Comparison with Base Line Approaches Together with the labeled features we also use some labeled examples. We used mallet library for our implementation. It has GE-FL and EM-MNB implemented in it. We vary the number ...
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A Massively Parallel Deep Rule Based Ensemble Classifier for Remote Sensing Scenes
... parallel rule are reported in Table ...DRB classifier can achieve 96%+ accuracy with a parallel training process of less than 6 ...per rule/class and the classification accuracy varies from ...DRB ...
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Active Deep Networks for Semi Supervised Sentiment Classification
... method based on deep neural net- work, this result proves the good learning ability of deep ...of semi-supervised learning and active learning based on deep architecture, ...
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Deep Generative Models for Semi-Supervised Machine Learning
... art semi-supervised probabilistic machine learning framework that can capture the unique patterns and cluster them accordingly to their respective ...a supervised classifier, learned from a ...
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Semi-Supervised Deep Neural Network for Network Intrusion Detection
... a semi-supervised SVM to classify the NSL- KDD dataset. Their semi-supervised method consisted of self-training where the SVM is trained on labeled data and then is used to classify unlabeled ...
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Hybrid Deep Belief Networks for Semi supervised Sentiment Classification
... state-of-the-art semi-supervised learning algorithms, such as SVM and DBN based methods, using just few labeled reviews, which demonstrate the effective of deep architecture for sentiment ...
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