1,275 research outputs found
Conformal Prediction: a Unified Review of Theory and New Challenges
In this work we provide a review of basic ideas and novel developments about
Conformal Prediction -- an innovative distribution-free, non-parametric
forecasting method, based on minimal assumptions -- that is able to yield in a
very straightforward way predictions sets that are valid in a statistical sense
also in in the finite sample case. The in-depth discussion provided in the
paper covers the theoretical underpinnings of Conformal Prediction, and then
proceeds to list the more advanced developments and adaptations of the original
idea.Comment: arXiv admin note: text overlap with arXiv:0706.3188,
arXiv:1604.04173, arXiv:1709.06233, arXiv:1203.5422 by other author
Transductive Learning with String Kernels for Cross-Domain Text Classification
For many text classification tasks, there is a major problem posed by the
lack of labeled data in a target domain. Although classifiers for a target
domain can be trained on labeled text data from a related source domain, the
accuracy of such classifiers is usually lower in the cross-domain setting.
Recently, string kernels have obtained state-of-the-art results in various text
classification tasks such as native language identification or automatic essay
scoring. Moreover, classifiers based on string kernels have been found to be
robust to the distribution gap between different domains. In this paper, we
formally describe an algorithm composed of two simple yet effective
transductive learning approaches to further improve the results of string
kernels in cross-domain settings. By adapting string kernels to the test set
without using the ground-truth test labels, we report significantly better
accuracy rates in cross-domain English polarity classification.Comment: Accepted at ICONIP 2018. arXiv admin note: substantial text overlap
with arXiv:1808.0840
Hedging predictions in machine learning
Recent advances in machine learning make it possible to design efficient
prediction algorithms for data sets with huge numbers of parameters. This paper
describes a new technique for "hedging" the predictions output by many such
algorithms, including support vector machines, kernel ridge regression, kernel
nearest neighbours, and by many other state-of-the-art methods. The hedged
predictions for the labels of new objects include quantitative measures of
their own accuracy and reliability. These measures are provably valid under the
assumption of randomness, traditional in machine learning: the objects and
their labels are assumed to be generated independently from the same
probability distribution. In particular, it becomes possible to control (up to
statistical fluctuations) the number of erroneous predictions by selecting a
suitable confidence level. Validity being achieved automatically, the remaining
goal of hedged prediction is efficiency: taking full account of the new
objects' features and other available information to produce as accurate
predictions as possible. This can be done successfully using the powerful
machinery of modern machine learning.Comment: 24 pages; 9 figures; 2 tables; a version of this paper (with
discussion and rejoinder) is to appear in "The Computer Journal
ELM regime classification by conformal prediction on an information manifold
Characterization and control of plasma instabilities known as edge-localized modes (ELMs) is crucial for the operation of fusion reactors. Recently, machine learning methods have demonstrated good potential in making useful inferences from stochastic fusion data sets. However, traditional classification methods do not offer an inherent estimate of the goodness of their prediction. In this paper, a distance-based conformal predictor classifier integrated with a geometric-probabilistic framework is presented. The first benefit of the approach lies in its comprehensive treatment of highly stochastic fusion data sets, by modeling the measurements with probability distributions in a metric space. This enables calculation of a natural distance measure between probability distributions: the Rao geodesic distance. Second, the predictions are accompanied by estimates of their accuracy and reliability. The method is applied to the classification of regimes characterized by different types of ELMs based on the measurements of global parameters and their error bars. This yields promising success rates and outperforms state-of-the-art automatic techniques for recognizing ELM signatures. The estimates of goodness of the predictions increase the confidence of classification by ELM experts, while allowing more reliable decisions regarding plasma control and at the same time increasing the robustness of the control system
Projected Estimators for Robust Semi-supervised Classification
For semi-supervised techniques to be applied safely in practice we at least
want methods to outperform their supervised counterparts. We study this
question for classification using the well-known quadratic surrogate loss
function. Using a projection of the supervised estimate onto a set of
constraints imposed by the unlabeled data, we find we can safely improve over
the supervised solution in terms of this quadratic loss. Unlike other
approaches to semi-supervised learning, the procedure does not rely on
assumptions that are not intrinsic to the classifier at hand. It is
theoretically demonstrated that, measured on the labeled and unlabeled training
data, this semi-supervised procedure never gives a lower quadratic loss than
the supervised alternative. To our knowledge this is the first approach that
offers such strong, albeit conservative, guarantees for improvement over the
supervised solution. The characteristics of our approach are explicated using
benchmark datasets to further understand the similarities and differences
between the quadratic loss criterion used in the theoretical results and the
classification accuracy often considered in practice.Comment: 13 pages, 2 figures, 1 tabl
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