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Book ChapterDOI

Direct Methods in Statistical Learning Theory

Vladimir Vapnik
- pp 225-265
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TLDR
In this chapter, a new approach to the main problems of statistical learning theory is introduced: pattern recognition, regression estimation, and density estimation.
Abstract
In this chapter we introduce a new approach to the main problems of statistical learning theory: pattern recognition, regression estimation, and density estimation.

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Journal ArticleDOI

Predicting movie Box-office revenues by exploiting large-scale social media content

TL;DR: The experimental results show that large-scale social media content is correlated withMovie box-office revenues and that the purchase intention of users can lead to more accurate movie box- office revenue predictions.
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Deep Learning Volatility

TL;DR: A neural network based calibration method that performs the calibration task within a few milliseconds for the full implied volatility surface and brings several numerical pricers and model families within the scope of applicability in industry practice.
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Recognition of emotional states using EEG signals based on time-frequency analysis and SVM classifier

TL;DR: An emotion detection method based on time-frequency domain statistical features based on electroencephalogram (EEG) technique that outperforms than the state-of-art methods by exhibiting higher accuracy.
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Deep density ratio estimation for change point detection.

TL;DR: This work proposes new objective functions to train deep neural network based density ratio estimators and applies it to a change point detection problem and shows that the method can still support other neural network architectures, such as convolutional networks.