Furthermore, a new tensor factorization approach is proposed to speed up the model and avoid overfitting .

Moreover, we propose a tensor factorization approach that effectively improves the model efficiency and avoids the risk of overfitting .

by the design of features and the number of features could be so large that the result models are too large for practical use and prone to overfit on training corpus.

Moreover, we propose a tensor factorization approach that effectively improves the model efficiency and prevents from overfitting .

Not only does this approach improve the efficiency of our model but also it avoids the risk of overfitting .

Moreover, the additional tensor could bring millions of parameters to the model which makes the model suffer from the risk of overfitting .

As long as 7“ is small enough, the factorized tensor operation would be much faster than the un-factorized one and the number of free parameters would also be much smaller, which prevent the model from overfitting .

However, given the small size of their tensor matrix, they do not have the problem of high time cost and overfitting problem as we faced in modeling a sequence labeling task like Chinese word segmentation.

That’s why we propose to decrease computational cost and avoid overfitting with tensor factorization.

By introducing tensor factorization into the neural network model for sequence labeling tasks, the model training and inference are speeded up and overfitting is prevented.

the approaches that completely depend on the labeled data are likely to run into overfitting .

Linear SVM performed better than the other two, since the large-margin constraint together with the linear model constraint can alleviate overfitting .

When we build a naive model to detect relations, the model tends to overfit for the labeled data.

Integration of the unlabeled data can help solve overfitting problems when the labeled data is not sufficient.

The second term is useful to bound the mapping function f and prevents overfitting from happening.

0 The algorithm exploits unlabeled data, which helps prevent “overfitting” from happening.

On the other hand, once such assumptions are removed, another problem arises — they might be prone to errors, and suffer from the overfitting issue.

Therefore, coping with the tradeoff between expressiveness and overfitting , seems to be rather important in statistical approaches that capture stochastic dependency.

This is of crucial importance to modeling text data: instead of using the classic bag-of-words representation that uses raw counts, we are now working with uniform marginal CDFs, which helps coping with the overfitting issue due to noise and data sparsity.

The second issue is about overfitting .

On the pre-2009 dataset, we see that the linear regression and linear SVM perform reasonably well, but the Gaussian kernel SVM performs less well, probably due to overfitting .

In speech and language processing, smoothing is essential to reduce overfitting , and Kneser-Ney (KN) smoothing (Kneser and Ney, 1995; Chen and Goodman, 1999) has consistently proven to be among the best-performing and most widely used methods.

It also contains most of the model’s parameters and is where overfitting occurs most.

However, MLE is prone to overfitting , one symptom of which is the “garbage collection” phenomenon where a rare English word is wrongly aligned to many French words.

To reduce overfitting , we use expected KN smoothing during the M step.

This constraint prevents each model from overfitting to a particular direction and leads to global optimization across alignment directions.

In addition, an [2 regularization term is added to the objective to prevent the model from overfitting the training data.

The proposed constraint penalizes overfitting to a particular direction and enables two directional models to optimize across alignment directions globally.