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Ridge Regression Learning Algorithm in Dual Variables
TLDR
In this paper we study a dual version of the Ridge Regression procedure which allows the use of kernel functions, as used in Support Vector methods. Expand
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Probability and Finance: It's Only a Game!
Preface. Probability and Finance as a Game. PROBABILITY WITHOUT MEASURE. The Historical Context. The Bounded Strong Law of Large Numbers. Kolmogorov's Strong Law of Large Numbers. The Law of theExpand
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Aggregating strategies
  • V. Vovk
  • Computer Science
  • COLT '90
  • 1 July 1990
  • 698
  • 44
A tutorial on conformal prediction
TLDR
Conformal prediction is designed for an on-line setting in which labels are predicted successively, each one being revealed before the next is predicted. Expand
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A game of prediction with expert advice
  • V. Vovk
  • Computer Science
  • COLT '95
  • 5 July 1995
We consider the following problem. At each point of discrete time the learner must make a prediction; he is given the predictions made by a pool of experts. Each prediction and the outcome, which isExpand
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Competitive On-line Statistics
Summary A radically new approach to statistical modelling, which combines mathematical techniques of Bayesian statistics with the philosophy of the theory of competitive on-line algorithms, hasExpand
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Inductive Confidence Machines for Regression
TLDR
The existing methods of predicting with confidence give good accuracy and confidence values, but quite often are computationally inefficient. Expand
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Algorithmic Learning in a Random World
Algorithmic Learning in a Random World describes recent theoretical and experimental developments in building computable approximations to Kolmogorov's algorithmic notion of randomness. Based onExpand
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Learning by Transduction
TLDR
We describe a method for predicting a classification of an object given classifications of the objects in the training set, assuming that the pairs object/classification are generated by an i.d. process from a continuous probability distribution. Expand
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Conformal Prediction for Reliable Machine Learning: Theory, Adaptations and Applications
TLDR
Conformal Predictions for Reliable Machine Learning: Theory, Adaptations and Applications captures the basic theory of the framework, demonstrates how to apply it to real-world problems, and presents several adaptations, including active learning, change detection, and anomaly detection. Expand
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