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training data

Tri Training for Authorship Attribution with Limited Training Data

Tri Training for Authorship Attribution with Limited Training Data

... the training data for accurate ...tri- training method to iteratively identify authors of unlabeled data to augment the training ...10 training documents per author, we ...

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Enriching SMT Training Data via Paraphrasing

Enriching SMT Training Data via Paraphrasing

... SMT training data by paraphrasing the source-side sentences of the bilingual parallel data through a statistical paraphrase generation ...size training corpora in terms of ...

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Optimising training data for ANNs with Genetic Algorithms

Optimising training data for ANNs with Genetic Algorithms

... The training set must be representative for the simulation period to improve inter- polation of ...the training data must contain high and low flows and in a rainfall-runoff model the data ...

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Reinforced Training Data Selection for Domain Adaptation

Reinforced Training Data Selection for Domain Adaptation

... different data and ...of) training instance at each step, where previous decision should influence later ...a training sample and the tar- get domain, and to guide the selection process with the ...

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Training Data Enrichment for Infrequent Discourse Relations

Training Data Enrichment for Infrequent Discourse Relations

... In this paper, we investigate the underfitting hypothesis and study how to improve the situation. Dif- ferent discourse relations are usually unevenly distributed in a dataset, and some of them occur much less frequently ...

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The potential of synthetic training data for training deep learning models

The potential of synthetic training data for training deep learning models

... synthetic training data for training deep learning ...synthetic training for training deep learning ...for training deep learning models such as data scarcity and the ...

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Generating Training Data for Medical Dictations

Generating Training Data for Medical Dictations

... The results of this experiment provide additional support for using automatically generated semi- literal transcriptions as a viable (and possibly superior) substitute for literal data. The fact that three ...

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Intelligent Selection of Language Model Training Data

Intelligent Selection of Language Model Training Data

... model training data, but only if the training data is reasonably well-matched to the desired ...in-domain data is ...in-domain data with other data, either by combining ...

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How Training Data Influence The Recognition Performance?

How Training Data Influence The Recognition Performance?

... of training data on detection ...the training images have a great impact on detection ...small-sized training images, the detection performance of the detector ...

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Effective Selection of Translation Model Training Data

Effective Selection of Translation Model Training Data

... Additionally, it turns out that our methods (TM, TM+LM and Bidirectional TM+LM) are indeed more effective in selecting domain- relevant sentence pairs. In the end-to-end SMT evaluation, TM selects top 600k sentence pairs ...

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Benchmark Synthetic Training Data for Artificial

Benchmark Synthetic Training Data for Artificial

... incremental capacity analysis and state of health estimation of lithium-ion battery. Incremental capacity analysis and close-to-equilibrium 468[r] ...

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Data point selection for self training

Data point selection for self training

... sparse data problems has gained a lot of at- tention in recent ...While training a generative parsing model on its own output (Charniak, 1997; Steedman et ...annotated training data is ...

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CNN based Image Classification Model

CNN based Image Classification Model

... From the graph it can be inferred that for the training data initially the accuracy is low, with the increase in the number of epochs the accuracy increases. After the first epoch the weights and biases are ...

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Training Automatic Transliteration Models on DBPedia Data

Training Automatic Transliteration Models on DBPedia Data

... (Matthews, 2007) approaches transliteration very similarly to the way we do. Like us, he trains machine translation Moses models on parallel lists of proper names. The lan- guage pairs for which he obtains translitera- ...

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Topic Classification of Blog Posts Using Distant Supervision

Topic Classification of Blog Posts Using Distant Supervision

... Discussion. Our motivation to use Freebase and Wikipedia comes from their large size and free availability, besides the fact these are fairly high quality resources–given the dedication of their contributors. It should ...

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Handling Noisy Training and Testing Data

Handling Noisy Training and Testing Data

... Testing data is another story, ...noisy training data in order to better model real-world use, but it is unfair and unreasonable to have noise in the gold standard 5 , which causes an algorithm to be ...

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Extracting Opinion Targets in a Single and Cross Domain Setting with Conditional Random Fields

Extracting Opinion Targets in a Single and Cross Domain Setting with Conditional Random Fields

... different training - testing domain configurations we observe the fol- lowing: In isolation training data from the cameras domain consistently yields the best results regarding F-Measure when the ...

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Parameterisation Comparison for the Detection of Panic Disorder Using Time Frequency Transforms and Support Vector Machines

Parameterisation comparison for the detection of panic disorder using time-frequency transforms and support vector machines

... biomedical data. When splitting the data arbitrary in training and test data and ap- plying a support vector machine as described in the previ- ous section, we yield specific detection rates ...

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Feature Noising for Log Linear Structured Prediction

Feature Noising for Log Linear Structured Prediction

... NLP models have many and sparse features, and regularization is key for balancing model overfitting versus underfitting. A recently re- popularized form of regularization is to gen- erate fake training data ...

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Featureless Classification Model Training Algorithm Based On Similarity Measure

Featureless Classification Model Training Algorithm Based On Similarity Measure

... Similarity based or Distance based learning is practiced when the training data sets are not depicted in Euclidian space. When the samples are depicted as feature vectors but the appropriate association ...

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