[PDF] Top 20 Profiled SCA with a New Twist: Semi-supervised Learning
Has 10000 "Profiled SCA with a New Twist: Semi-supervised Learning" found on our website. Below are the top 20 most common "Profiled SCA with a New Twist: Semi-supervised Learning".
Profiled SCA with a New Twist: Semi-supervised Learning
... machine learning technique used to accurately classify both linearly separable and linearly inseparable data ...this new space, the samples can usually be classified with a higher ...a learning ... See full document
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End-user feature labeling: Supervised and semi-supervised approaches based on locally-weighted logistic regression
... the learning algorithm could ask the user to provide class labels for strategically chosen instances that would most inform the learning algorithm, as is done in traditional active learning [7, ... See full document
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Distributed Semi-supervised Learning with Kernel Ridge Regression
... optimal learning rate for DSKRR can be achieved for all 1 2 ≤ r ≤ 1 without assuming condition (11), provided additional unlabeled data are ...privacy-preserving learning when the data are stored in m ... See full document
22
A Review on Semi Supervised Text Classification
... the new material not be in the text overtly means that the system must have access to external information of some category, such as a knowledge base or an ontology, and be able to perform combinatory ... See full document
10
Semi Supervised Learning for Neural Keyphrase Generation
... pose semi-supervised keyphrase generation methods by leveraging both labeled data and large-scale unlabeled samples for ...self- learning algorithm, and then combined with labeled samples for ... See full document
12
Semi Supervised Active Learning for Sequence Labeling
... compare semi-supervised AL (SeSAL) with its fully supervised counterpart (FuSAL), using a passive learning scheme where examples are randomly selected (RAND) as ... See full document
9
Towards Automated Semi-Supervised Learning
... Meta-Learning with Enhanced Meta-Features Meta-learning (Brazdil et al. 2008) aims to reason about the performance of learning algorithms across different datasets. Specifically, in AutoML, we ... See full document
8
Three phase training to address data sparsity in Neural Machine Translation
... Data sparsity is a key problem in contemporary neural machine transla- tion (NMT) techniques, especially for resource-scarce language pairs. NMT models when coupled with large, high quality parallel corpora provide ... See full document
10
Unbiased Generative Semi-Supervised Learning
... examines learning the parameters of both a single gaussian and a GMM when labels are ...on semi-supervised learning, in particular from the point of view of the Hughes phenomenon (Hughes, ... See full document
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Semi-described and semi-supervised learning with Gaussian processes
... as learning pipelines that rely on correct propagation of uncertainty between each ...observations: semi-supervised learning, auto-regressive iterative fore- casting and, finally, a newly ... See full document
11
Large Margin Semi-supervised Learning
... as semi-supervised learning, which differs from a conventional “missing data” problem in that the size of unlabeled data greatly exceeds that of labeled data, and missing occurs only in ...In ... See full document
25
A New Sub-topic Clustering Method Based on Semi-supervised Learning
... In view of this, this paper presents a new method of sub-topic clustering based on semi-supervised learning. The algorithm first partition the set of sentences into disjoint subsets or ... See full document
8
Semi-Supervised Learning with Measure Propagation
... a new objective for graph-based semi-supervised learning based on minimizing the Kullback-Leibler divergence between discrete probability measures that encode class membership ...the ... See full document
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A Review on health care examination records using data mining
... address new clustering problem to detect net-clusters on special heterogeneous network with star network ...class semi-supervised learning problem with predefined classes, and thus have no ... See full document
5
Abbreviation Detection in Vietnamese Clinical Texts
... a semi-supervised learning approach that uses level-wise feature engineering to construct an abbreviation identifier, from using a small set of labeled clinical texts and exploiting a larger set of ... See full document
17
Semi Supervised Learning for Relation Extraction
... This paper proposes a new effective and efficient semi-supervised learning method in relation ex- traction. First, a moderate number of weighted support vectors are bootstrapped from all the ... See full document
8
Graph based Learning for Statistical Machine Translation
... as opposed to entire graphs. String kernel represen- tations have been used in MT (Szedmak, 2007) in a kernel regression based framework, which, how- ever, was an entirely supervised framework. Finally, our ... See full document
9
Coupling Semi Supervised Learning of Categories and Relations
... the learning of predicates by using positive exam- ples of one predicate as negative examples for oth- ers has been shown to help limit this drift (Riloff and Jones, 1999; Yangarber, ... See full document
9
Semi Supervised Learning for Neural Machine Translation
... In this paper, we propose semi-supervised learning for neural machine translation. Given la- beled (i.e., parallel corpora) and unlabeled (i.e., monolingual corpora) data, our approach jointly trains ... See full document
10
Power Transformer Fault Diagnosis based on Deep Learning
... Deep belief network is a neural network which is composed of a plurality of restricted Boltzmann machines (RBM) with multi hidden layers. Its core is to optimize the connection weights of the deep neural network by using ... See full document
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