7,231 research outputs found
Cross-Lingual Adaptation using Structural Correspondence Learning
Cross-lingual adaptation, a special case of domain adaptation, refers to the
transfer of classification knowledge between two languages. In this article we
describe an extension of Structural Correspondence Learning (SCL), a recently
proposed algorithm for domain adaptation, for cross-lingual adaptation. The
proposed method uses unlabeled documents from both languages, along with a word
translation oracle, to induce cross-lingual feature correspondences. From these
correspondences a cross-lingual representation is created that enables the
transfer of classification knowledge from the source to the target language.
The main advantages of this approach over other approaches are its resource
efficiency and task specificity.
We conduct experiments in the area of cross-language topic and sentiment
classification involving English as source language and German, French, and
Japanese as target languages. The results show a significant improvement of the
proposed method over a machine translation baseline, reducing the relative
error due to cross-lingual adaptation by an average of 30% (topic
classification) and 59% (sentiment classification). We further report on
empirical analyses that reveal insights into the use of unlabeled data, the
sensitivity with respect to important hyperparameters, and the nature of the
induced cross-lingual correspondences
Dropout Training as Adaptive Regularization
Dropout and other feature noising schemes control overfitting by artificially
corrupting the training data. For generalized linear models, dropout performs a
form of adaptive regularization. Using this viewpoint, we show that the dropout
regularizer is first-order equivalent to an L2 regularizer applied after
scaling the features by an estimate of the inverse diagonal Fisher information
matrix. We also establish a connection to AdaGrad, an online learning
algorithm, and find that a close relative of AdaGrad operates by repeatedly
solving linear dropout-regularized problems. By casting dropout as
regularization, we develop a natural semi-supervised algorithm that uses
unlabeled data to create a better adaptive regularizer. We apply this idea to
document classification tasks, and show that it consistently boosts the
performance of dropout training, improving on state-of-the-art results on the
IMDB reviews dataset.Comment: 11 pages. Advances in Neural Information Processing Systems (NIPS),
201
A Taxonomy of Big Data for Optimal Predictive Machine Learning and Data Mining
Big data comes in various ways, types, shapes, forms and sizes. Indeed,
almost all areas of science, technology, medicine, public health, economics,
business, linguistics and social science are bombarded by ever increasing flows
of data begging to analyzed efficiently and effectively. In this paper, we
propose a rough idea of a possible taxonomy of big data, along with some of the
most commonly used tools for handling each particular category of bigness. The
dimensionality p of the input space and the sample size n are usually the main
ingredients in the characterization of data bigness. The specific statistical
machine learning technique used to handle a particular big data set will depend
on which category it falls in within the bigness taxonomy. Large p small n data
sets for instance require a different set of tools from the large n small p
variety. Among other tools, we discuss Preprocessing, Standardization,
Imputation, Projection, Regularization, Penalization, Compression, Reduction,
Selection, Kernelization, Hybridization, Parallelization, Aggregation,
Randomization, Replication, Sequentialization. Indeed, it is important to
emphasize right away that the so-called no free lunch theorem applies here, in
the sense that there is no universally superior method that outperforms all
other methods on all categories of bigness. It is also important to stress the
fact that simplicity in the sense of Ockham's razor non plurality principle of
parsimony tends to reign supreme when it comes to massive data. We conclude
with a comparison of the predictive performance of some of the most commonly
used methods on a few data sets.Comment: 18 pages, 2 figures 3 table
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