Results 111 to 120 of about 15,562 (261)

Fast Schemes for Computing Similarities between Gaussian HMMs and Their Applications in Texture Image Classification

EURASIP Journal on Advances in Signal Processing, 2005
An appropriate definition and efficient computation of similarity (or distance) measures between two stochastic models are of theoretical and practical interest.
Chen Ling, Man Hong
doaj   +1 more source

A Non‐Parametric Framework for Correlation Functions on Product Metric Spaces

International Statistical Review, EarlyView.
Summary We propose a non‐parametric framework for analysing data defined over products of metric spaces, a versatile class encountered in various fields. This framework accommodates non‐stationarity and seasonality and is applicable to both local and global domains, such as the Earth's surface, as well as domains evolving over linear time or time ...
Pier Giovanni Bissiri, Bernardo Nipoti, Emilio Porcu, Philip White   +3 more
wiley   +1 more source

Semispectral Measures and Feller markov Kernels

, 2012
We give a characterization of commutative semispectral measures by means of Feller and Strong Feller Markov kernels. In particular: {itemize} we show that a semispectral measure $F$ is commutative if and only if there exist a self-adjoint operator $A$ and a Markov kernel $μ_{(\cdot)}(\cdot):Γ\times\mathcal{B}(\mathbb{R})\to[0,1]$, $Γ\subsetσ(A)$, $E(Γ)=
openaire   +2 more sources

Missing Values in Time Series: A Brief Review and a New Versatile Imputation Method

International Statistical Review, EarlyView.
Summary Missing data can significantly hamper standard time series analysis, yet they occur frequently in applications. In this paper, we briefly review some available methods for handling missing values and introduce the temporal Wasserstein imputation, a novel method for imputing missing data in time series.
Shuo‐Chieh Huang, Tengyuan Liang, Ruey S. Tsay   +2 more
wiley   +1 more source

Deep reinforcement learning for multi‐restart metaheuristics: an environment design for a hybrid of unbiased exploratory search and covariance matrix adaptation evolution strategy

International Transactions in Operational Research, EarlyView.
Abstract Multi‐restart metaheuristics can be highly effective for complex optimization problems, yet their performance depends critically on how restarts and algorithmic parameters are selected. This paper introduces a reinforcement learning approach for managing restart‐level decisions and parameter configurations in the UES–CMA‐ES hybrid ...
Antonio Bolufé‐Röhler, Bowen Xu
wiley   +1 more source

A Hybrid Large-Kernel CNN and Markov Feature Framework for Remaining Useful Life Prediction

Machines
Remaining Useful Life (RUL) prediction has become a crucial component in predictive maintenance and condition-based operation with the rapid advancement of industrial automation and the increasing complexity of mechanical systems.
Yuke Wang, Che Su, Peng Wang, Junquan Zhen, Dong Wang   +4 more
doaj   +1 more source

Mixing It Up: Inflation at Risk

Journal of Money, Credit and Banking, EarlyView.
Abstract Understanding how risk factors shape the economic outlook is essential for guiding policy decisions. This paper develops a flexible framework that decomposes distributional risk forecasts of macro‐economic variables into underlying contributions and supports the construction of interpretable risk measures.
MAXIMILIAN SCHRÖDER
wiley   +1 more source

Covariance Kernels of Gaussian Markov Processes

, 2015
32 ...
openaire   +2 more sources

Brain tissue classification in hyperspectral images using multistage diffusion features and transformer

Journal of Microscopy, EarlyView.
Abstract Brain surgery is a widely practised and effective treatment for brain tumours, but accurately identifying and classifying tumour boundaries is crucial to maximise resection and avoid neurological complications. This precision in classification is essential for guiding surgical decisions and subsequent treatment planning.
Neetu Sigger, Tuan T. Nguyen, Gianluca Tozzi   +2 more
wiley   +1 more source

Empirical‐Process Limit Theory and Filter Approximation Bounds for Score‐Driven Time Series Models

Journal of Time Series Analysis, EarlyView.
ABSTRACT This article examines the filtering and approximation‐theoretic properties of score‐driven time series models. Under specific Lipschitz‐type and tail conditions, new results are derived, leading to maximal and deviation inequalities for the filtering approximation error using empirical process theory.
Enzo D'Innocenzo
wiley   +1 more source