[PDF] Top 20 Automatically classifying test results by semi-supervised learning
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Automatically classifying test results by semi-supervised learning
... Sed test data (input/output pairs) consists of a com- mand line which contains 2 main parts: the parameters iden- tifying the operations to be performed and a text file that needs to be modified which therefore ... See full document
11
Semi-described and semi-supervised learning with Gaussian processes
... in semi-supervised learning ...at test time. Propagating the test input uncertainty through a non-linear GP results in a non-Gaussian predictive density, but Girard et ... See full document
11
Towards Automated Semi-Supervised Learning
... Machine Learning (AutoML) aims to build an appropriate machine learning model for any unseen dataset automatically, ...on supervised learning. In many applications, however, ... See full document
8
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 ...When results are ... See full document
9
Network construction and applications for semi-supervised learning
... The results are shown in Table ...classification results using 10 and 100 labeled vertices, besides a parameter (in ...post-hoc test [6] was executed to verify the possibility of detecting ... See full document
7
Evaluation of Supervised Machine Learning for Classifying Video Traffic
... self- learning unsupervised classifier named ...clusters. Results from Grimaudo et al. (2014) indicated that SeLeCT could semi-automatically classify traffic, with the use of seed data derived ... See full document
133
Semi supervised Multitask Learning for Sequence Labeling
... The bottom row shows the performance of the language modeling objective when added on top of the baseline model, along with dropout on word embeddings. This architecture outperforms the baseline on all benchmarks, ... See full document
10
Distribution-Based Semi-Supervised Learning for Activity Recognition
... its semi-supervised fashion. For semi-supervised methods, the ranking is DSSL > LapSVM ≈ GLSVM ≈ 5TSVM > SSKLR, which demonstrates the prevalence of graph-based methods over logistic ... See full document
8
Semi-supervised learning of deep neural networks
... stratified test set of size 5000 for ...if semi-supervised learning could overcome this problem, it could be ...fully supervised learning this way using any of the implemented ... See full document
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Training spamassassin with active semi-supervised learning
... Each test is associated with a score: the score is zero if the related characteristic is not present, while it is non-zero if such characteristic is ...each test and for the decision ...which ... See full document
8
Semi-supervised learning
... report results that are the average of 10 trials over random draws of M unlabeled and n la- beled ...[Global]: supervised learner trained on all of the labeled data, ignoring unlabeled ...our semi- ... See full document
8
Regular graph construction for semi-supervised learning
... To test this hipotesis, that regular graph can be better for SSL, we introduce a new method for generation of graphs with no ...classification results from UCI [1] and Chapelle [2] data sets show the ... See full document
6
Cluster-based semi-supervised ensemble learning
... instance. Learning an unlabelled instance involves assessing its neighbourhood, which improves the reliability of the estimated label that is assigned to that ... See full document
201
Deep Learning via Semi-Supervised Embedding
... deep learning by leveraging existing ideas from semi-supervised algorithms developed in shallow ...a supervised learner to perform semi-supervised learning, such as is ... See full document
17
Semi Supervised Learning for Neural Keyphrase Generation
... Keyphrase Extraction and Generation. Early work mostly focuses on the keyphrase extrac- tion task, and a two-step strategy is typically designed. Specifically, a large pool of candi- date phrases are first extracted ... See full document
12
Paraphrase Generation for Semi Supervised Learning in NLU
... It is crucial for applications to add support for new functionalities and improve them con- tinuously. An efficient method for this is semi- supervised learning (SSL), where the model learns from ... See full document
10
Semi Supervised Learning for Neural Machine Translation
... While end-to-end neural machine transla- tion (NMT) has made remarkable progress recently, NMT systems only rely on par- allel corpora for parameter estimation. Since parallel corpora are usually limited in quantity, ... See full document
10
Semi-supervised learning with explicit relationship regularization
... for semi- supervised learning covering image digits (USPS), EEG signals (BCI), newsgroup categories (Text, Pcmac, Real-sim) and news reports (CCAT, GCAT) [4, ... See full document
9
Sparse Semi-supervised Learning Using Conjugate Functions
... sparse semi-supervised learning approach: sparse multi-view SVMs, where the conjugate functions play a central role in reformulating the optimization ...experimental results of the sparse ... See full document
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Semi-Supervised Learning Using Greedy Max-Cut
... In order to handle such challenging labeling conditions, we first extend the existing GSSL for- mulation by casting it as a bivariate optimization problem over the classification function and the labels. Then we ... See full document
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