Results 271 to 280 of about 3,141,344 (325)

Empirical entropy for right censored data

Acta Mathematicae Applicatae Sinica, English Series, 2015
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Zhao, Guo-qing, Liang, Wei, He, Shu-yuan
semanticscholar   +4 more sources

Fast Approximation of Empirical Entropy via Subsampling

Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 2019
Empirical entropy refers to the information entropy calculated from the empirical distribution of a dataset. It is a widely used aggregation function for knowledge discovery, as well as the foundation of other aggregation functions such as mutual information.
Chi Wang, Bailu Ding
openaire   +2 more sources

Blockwise empirical entropy tests for time series regressions

Journal of Time Series Analysis, 2005
Abstract.  This paper shows how the empirical entropy (also known as exponential likelihood or non‐parametric tilting) method can be used to test general parametric hypothesis in time series regressions. To capture the weak dependence of the observations, the paper uses blocking techniques which are also used in the bootstrap literature on time series.
Francesco Bravo
openaire   +2 more sources

Emotion recognition using empirical mode decomposition and approximation entropy

Computers & Electrical Engineering, 2018
Abstract Automatic human emotion recognition is a key technology for human-machine interaction. In this paper, we propose an electroencephalogram (EEG) feature extraction method that leverages empirical mode decomposition and Approximation Entropy. In our proposed method, Empirical Mode Decomposition (EMD) is used to process EEG signals after data ...
Tian Chen, Sihang Ju, Xiaohui Yuan, Mohamed Elhoseny, Fuji Ren, Mingyan Fan, Zhangang Chen   +6 more
openaire   +2 more sources

Efficient Approximate Algorithms for Empirical Entropy and Mutual Information

SIGMOD Conference, 2021
Empirical entropy is a classic concept in data mining and the foundation of many other important concepts like mutual information. However, computing the exact empirical entropy/mutual information on large datasets can be expensive.
Xingguang Chen, Sibo Wang
semanticscholar   +1 more source

RePair and All Irreducible Grammars are Upper Bounded by High-Order Empirical Entropy

IEEE Transactions on Information Theory, 2019
Irreducible grammars are a class of context-free grammars with well-known representatives, such as Repair (with a few tweaks), Longest Match, Greedy, and Sequential.
Carlos Ochoa, G. Navarro
semanticscholar   +1 more source

Constructing Multi-scale Entropy Based on the Empirical Mode Decomposition(EMD) and its Application in Recognizing Driving Fatigue.

Journal of Neuroscience Methods, 2020
BACKGROUND Fatigue is one of the important factors in traffic accidents. Hence, it is necessary to devise methods to detect the fatigue and apply practical fatigue detection solutions for drivers.
Shuli Zou, Taorong Qiu, Peifan Huang, Xiaoming Bai, Chao Liu   +4 more
semanticscholar   +1 more source

Asymptotic results for runs and empirical cumulative entropies

Journal of Statistical Planning and Inference, 2015
The paper under review deals with sequences of runs and of empirical cumulative entropies. The authors utilize the definition of runs based on differences and ratios of consecutive order statistics as in [\textit{S. Eryilmaz} and \textit{A. Stepanov}, ``Runs in an ordered sequence of random variables'', Metrika 67, No. 3, 299--311 (2008; Zbl 1357.60037)
Giuliano, R, MACCI, CLAUDIO, PACCHIAROTTI, BARBARA   +2 more
openaire   +4 more sources

The Entropy Mechanism of Reinforcement Learning for Reasoning Language Models

arXiv.org
This paper aims to overcome a major obstacle in scaling RL for reasoning with LLMs, namely the collapse of policy entropy. Such phenomenon is consistently observed across vast RL runs without entropy intervention, where the policy entropy dropped sharply
Ganqu Cui, Yuchen Zhang, Jiacheng Chen, Lifan Yuan, Zhi Wang, Yuxin Zuo, Haozhan Li, Yuchen Fan, Huayu Chen, Weize Chen, Zhiyuan Liu, Hao Peng, Lei Bai, Wanli Ouyang, Yu Cheng, Bowen Zhou, Ning Ding   +16 more
semanticscholar   +1 more source

Data decomposition method combining permutation entropy and spectral substitution with ensemble empirical mode decomposition

Measurement, 2019
As the majority of real signals (e.g. the dynamic information obtained during bridge monitoring) are nonstationary and nonlinear, empirical mode decomposition (EMD) and its derivatives have become popular research topics in recent years worldwide ...
Shengxiang Huang, Xinpeng Wang, Chenfeng Li, Chao Kang   +3 more
semanticscholar   +1 more source