208 research outputs found
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
Application of Kolmogorov complexity and universal codes to identity testing and nonparametric testing of serial independence for time series
We show that Kolmogorov complexity and such its estimators as universal codes
(or data compression methods) can be applied for hypotheses testing in a
framework of classical mathematical statistics. The methods for identity
testing and nonparametric testing of serial independence for time series are
suggested.Comment: submitte
On-line predictive linear regression
We consider the on-line predictive version of the standard problem of linear
regression; the goal is to predict each consecutive response given the
corresponding explanatory variables and all the previous observations. We are
mainly interested in prediction intervals rather than point predictions. The
standard treatment of prediction intervals in linear regression analysis has
two drawbacks: (1) the classical prediction intervals guarantee that the
probability of error is equal to the nominal significance level epsilon, but
this property per se does not imply that the long-run frequency of error is
close to epsilon; (2) it is not suitable for prediction of complex systems as
it assumes that the number of observations exceeds the number of parameters. We
state a general result showing that in the on-line protocol the frequency of
error for the classical prediction intervals does equal the nominal
significance level, up to statistical fluctuations. We also describe
alternative regression models in which informative prediction intervals can be
found before the number of observations exceeds the number of parameters. One
of these models, which only assumes that the observations are independent and
identically distributed, is popular in machine learning but greatly underused
in the statistical theory of regression.Comment: 34 pages; 6 figures; 1 table. arXiv admin note: substantial text
overlap with arXiv:0906.312
Multi-level conformal clustering:A distribution-free technique for clustering and anomaly detection
In this work we present a clustering technique called multi-level conformal clustering (MLCC). The technique is hierarchical in nature because it can be performed at multiple significance levels which yields greater insight into the data than performing it at just one level. We describe the theoretical underpinnings of MLCC, compare and contrast it with the hierarchical clustering algorithm, and then apply it to real world datasets to assess its performance. There are several advantages to using MLCC over more classical clustering techniques: Once a significance level has been set, MLCC is able to automatically select the number of clusters. Furthermore, thanks to the conformal prediction framework the resulting clustering model has a clear statistical meaning without any assumptions about the distribution of the data. This statistical robustness also allows us to perform clustering and anomaly detection simultaneously. Moreover, due to the flexibility of the conformal prediction framework, our algorithm can be used on top of many other machine learning algorithms
- …