Stochastic Least Error Rate Training for Classifiers
Stochastic margin-based structure learning of Bayesian network classifiers
... a b s t r a c t The margin criterion for parameter learning in graphical models gained significant impact over the last years. We use the maximum margin score for discriminatively optimizing the structure of Bayesian ...
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Adaptive Least Error Rate Algorithm for Neural Network Classifiers
... W e adopt the MER criterion and develop a stochastic gradient adaptive MER algorithm for training neural network classiers. W e employ an adap- tive strategy that is very similar to the one used for ...
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Regularization and Search for Minimum Error Rate Training
... 6 Conclusions We have presented two methods for improving the performance of MERT. The first is a novel stochas- tic search strategy that appears to make better use of Och (2003)’s algorithm for finding the global min- ...
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Training Highly Multiclass Classifiers
... time, classifiers choose only the highest-ranked class discriminant as a class label, and thus the fact that AUC sampling updates more often on lower-ranked classes is likely the key reason that WARP sampling ...
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Training genetic programming classifiers by vicinal-risk minimization
... test error versus vicinal risk; the shaded region shows the envelope of correspondences between test error and vicinal risk which can be observed in Figure ...test error. GP is, of course, a ...
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Stochastic Least Symbol Error Rate Adaptive Equalization for Pulse Amplitude Modulation
... GroupS. Chenr[r] ...
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Stabilizing Minimum Error Rate Training
... The point of this procedure is to bypass di- rect search for the weights that result in maxi- mum BLEU score, which would involve decoding using many different sets of weights in order to find which ones gave the best ...
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Minimum Error Rate Training by Sampling the Translation Lattice
... This in turn means that, for any given sentence, its contribution to BLEU changes only when the value of the parameters change in such a way that the sen- tence ranking first according to the model switches from one to ...
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Asymmetric least squares support vector machine classifiers
... corresponding classifiers obtained by LS-SVM and L2-SVM are given by solid and dashed lines, ...small training misclassification error are two desired targets for a good ...
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Lattice based Minimum Error Rate Training for Statistical Machine Translation
... in G 2 . The merged node is an articulation point whose removal would disconnect both subgraphs, and hence, all paths in G 1 that start at the source node s and stop in the sink node t lead through this articulation ...
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On the Rate of Convergence of Regularized Boosting Classifiers
... The rate of convergence of the probability of misclassification is ...of error converges to the Bayes risk at a rate faster than n −(V+2)/(4(V+1)) where V is the VC dimension of the “base” class ...
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Training Effective Node Classifiers for Cascade Classification
... Cascade classifiers are widely used in real- time object ...classification error rate, a classifier in each node of the cascade is required to achieve an extremely high detection rate and ...
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The Stochastic Error Rate Estimation of Prediction Distributions
... This work is licensed under the Creative Commons Attribution International License (CC BY). http://creativecommons.org/licenses/by/4.0/ Abstract The estimation of claims reserves is usually done by applying techniques ...
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Regularized Minimum Error Rate Training
... To increase the size of the hypothesis space, subse- quent work (Macherey et al., 2008) instead operated on lattices, but this paper focuses on M -best lists. A crucial observation is that the unsmoothed error ...
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Optimal Search for Minimum Error Rate Training
... This paper makes the following contributions. To our knowledge, it is the first known exact search algorithm for optimizing task loss on N -best lists in general dimensions. We also present an approximate version of ...
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Minimum Error Rate Training in Statistical Machine Translation
... Figure 1: Shape of error count and smoothed error count for two different model parameters. These curves have been computed on the development corpus (see Section 7, Table 1) using alternatives per source ...
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Feasibility of Human in the loop Minimum Error Rate Training
... will involve collecting data for thousands of al- ternative translations for several hundreds source sentences. Based on our analysis, this it should be cost-effective to solicit these judgments using AMT. After ...
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Transductive Minimum Error Rate Training for Statistical Machine Translation
... We can also review the roles that the develop- ment and test datasets play in the procedure of avoiding over-training. The reason for that we transductively generate translations as pseudo ref- erences for test ...
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Benchmarking least squares support vector machine classifiers.
... at least as good as LS-SVMs with linear kernel which, in combination with the sparse approximation procedure, also offers the possibility for knowledge discovery in the ...
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Grammatical Error Correction: Machine Translation and Classifiers
... An MT system misses these errors because it is limited to edits within short spans. Examples (3), (4), and (5) illustrate verb mistakes for which the correct replacements were not observed in train- ing but that are ...
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