large model training sets
Bayesian system identification of dynamical systems using large sets of training data: A MCMC solution
... the model evi- dence/generate samples from the posterior model distribution simultaneously – these include Reversible Jump MCMC [8], Tran- sitional MCMC (TMCMC) [9], Nested Sampling [10] and Asymp- ...
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Training Data Sets Construction from Large Data Set for PCB Character Recognition
... the model performance improvement is very high compared with the increase of the ...This model advantage is that model complexity and computational burden do not increase ...SE-ResNeXt model, ...
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Efficient Generation of Power In Medium Voltage Direct Current Systems: Variable Speed Operation and Rectifier Considerations
... in model training times and the scalability that results from deconstructing the training data set into smaller, focused ...smaller training sets, a single model may be best ...
105
Post-processing radio-frequency signal based on deep learning method for ultrasonic microbubble imaging
... with large training data sets, a deep learning model based on U-net was trained to differentiate microbubble and tissue radio frequency (RF) signals; (2) Then, to eliminate the remaining ...
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Scalable Learning of Bayesian Network Classifiers
... the training data, making it a three-pass ...16 large data sets reveals that this out-of-core algorithm achieves competitive classification performance, and substantially better training and ...
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Joint Feature Selection in Distributed Stochastic Learning for Large Scale Discriminative Training in SMT
... to training on small tuning sets of a few thousand ...larger training samples should be benefi- cial to improve prediction also in ...tuning sets lies in the particular design of the features ...
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Jointly Modeling Aspects and Opinions with a MaxEnt LDA Hybrid
... entropy model into an unsupervised, gen- erative topic model, we could leverage syntactic fea- tures to help separate aspect and opinion ...our model on two large review data sets from ...
10
Log Linear Model for String Transformation Using Large Data Sets
... linear model, a training method for the model and an algorithm for generating the top k candidates using a non-dictionary approach which helps the approach to be accurate as well as ...linear ...
9
Unsupervised Discriminative Language Model Training for Machine Translation using Simulated Confusion Sets
... discriminative training procedure is proposed for estimating a language model (LM) for machine trans- lation ...language model (LM) constitutes a crucial com- ponent in many tasks such as machine ...
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Transliteration Mining Using Large Training and Test Sets
... generative model that attempts to generate all possible transliterations of a source word, given the character mappings between two languages, and restricting the output to words in the target language (Fei et ...
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A Note on Strong Laws of Large Numbers for Dependent Random Sets and Fuzzy Random Sets
... The layout of this paper is as follows. In Section 2, we give some basic definitions and properties, and the new dependence is proposed in Section 3. In the last section we show several SLLNs for a sequence of dependent ...
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Unsupervised Models for Coreference Resolution
... the percentage of coreference links in the reference partition that appear in the system partition; preci- sion is computed in the same fashion as recall, ex- cept that the roles of the reference partition and the system ...
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Using mixed objects in the training of object based image classifications
... The reduced mixed training data sets also afforded larger classification accuracy than the pure.. training data sets.[r] ...
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A Partial Least Squares based algorithm for parsimonious variable selection
... Results: We present an algorithm balancing the parsimony and the predictive performance of a model. The algorithm is based on variable selection using reduced-rank Partial Least Squares with a regularized ...
12
ImageSURF: An ImageJ Plugin for Batch Pixel Based Image Segmentation Using Random Forests
... the training process open, repeatable and able to incorporate large training sets created by multiple users across multiple sessions with the software of their ...
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4 Techniques for Analyzing Large Data Sets
... Traditional techniques fail because very large trees can exhibit what Goloboff [10] termed composite optima. The TBR algorithm can get stuck in local optima for many data sets with 30-50 taxa. But a tree ...
10
Supervised sampling for clustering large data sets
... A supervised way for obtaining subsets of the data is developed for use in applications involving large data sets. The suggested approach consists of the construction of a data- dependent partitioning on ...
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SEARCHING LARGE DATA SETS IN GRID COMPUTING
... This first paper in this part, Legion: teaching Learnt Building a Grid Operating System, describe the mass operating system-like Grid middleware, which offer a virtual machine interface covered over the Grid. The paper ...
6
Visualization of Diversity in Large Multivariate Data Sets
... data sets of (a) very low-, (b) medium-, and (c) very high-diversity visualized using the Diversity Map ...a large (more than 1000 objects), multivariate (more than 5 attributes) data set as one worth ...
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Optimization of Linear Filtering Model to Predict Post LASIK Corneal Smoothing Based on Training Data Sets
... ing model to represent the corneal change post-surgery using an optimization algorithm, based on retrospectively available clinical ...data sets that were not previously used for the ...
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