搜索结果: 1-15 共查到“filtering”相关记录48条 . 查询时间(0.062 秒)
On Incremental Sigma-Delta Modulation with Optimal Filtering
Sigma-Delta (Σ∆ ) incremental A/D converter optimal filter Σ∆ transfer function time-domain analysis
2015/8/12
The paper presents a quantization-theoretic framework for studying incremental Σ∆ data conversion systems. The framework makes it possible to efficiently compute the quantization intervals and h...
Slope One Predictors for Online Rating-Based Collaborative Filtering
Collaborative Filtering Recommender e-Commerce Data Mining Knowledge Discovery
2015/8/3
Rating-based collaborative filtering is the process of predicting how a user would rate a given item from other user ratings. We propose three related slope one schemes with predictors of the form f(x...
Improving Recommendation Quality by Merging Collaborative Filtering and Social Relationships
Collaborative Filtering Recommender Systems Social Networks Matrix Factorization
2015/7/21
Matrix Factorization techniques have been successfully applied to raise the quality of suggestions generated by Collaborative Filtering Systems (CFSs). Traditional CFSs based on Matrix Factorization o...
Bibliographic Review on Distributed Kalman Filtering
Distributed Kalman filtering (DKF) Self-tuning (ST) distributed fusion Kalman filter distributed particle filtering (DPF) distributed consensus (DC)-based estimation track-to-track fusion distributed networks (DN) multisensor data fusion systems (MSDF) distributed out-of-sequence measurements (OOSM) diffusion-based DKF
2015/7/10
In recent years, a compelling need has arisen to understand the effects of distributed information structures on estimation and filtering. In this paper, a bibliographical review on distributed Kalman...
l_1 Trend Filtering
detrending regularization Hodrick–Prescott fi ltering piecewise linear fi tting
2015/7/9
The problem of estimating underlying trends in time series data arises in a variety of disciplines. In this paper we propose a variation on Hodrick-Prescott (H-P) filtering, a widely used method for t...
Optimal Crowd-Powered Rating and Filtering Algorithms
Optimal Crowd-Powered Rating Filtering Algorithms
2015/7/9
We focus on crowd-powered filtering, i.e., filtering a large set of items using humans. Filtering is one of the most commonly used building blocks in crowdsourcing applications and systems. While solu...
A general approach of least squares estimation and optimal filtering
Least squares Optimal filtering Matched filter Noise Optimization Power Spectrum Density
2013/6/17
The least squares method allows fitting parameters of a mathematical model from experimental data. This article proposes a general approach of this method. After introducing the method and giving a fo...
Optimal filtering and the dual process
auxiliary variables Bayesian conjugacy Dirichlet process finite mixture models Cox-Ingersoll-Ross process hidden Markov model,Kalman filters
2013/6/14
We link optimal filtering for hidden Markov models to the notion of duality for Markov processes. We show that when the signal is dual to a process that has two components, one deterministic and one a...
Infinite-dimensional Bayesian filtering for detection of quasi-periodic phenomena in spatio-temporal data
Infinite-dimensional Bayesian filtering detection of quasi-periodic phenomena spatio-temporal data
2013/4/27
This paper introduces a spatio-temporal resonator model and an inference method for detection and estimation of nearly periodic temporal phenomena in spatio-temporal data. The model is derived as a sp...
Efficient particle filtering through residual nudging
Efficient particle filtering residual nudging
2013/5/2
We introduce an auxiliary technique, called residual nudging, to the particle filter to enhance its performance in cases that it performs poorly. The main idea of residual nudging is to monitor, and i...
State estimation under non-Gaussian Levy noise: A modified Kalman filtering method
Kalman filter modified Kalman filter Non-Gaussiannoise L′evy noise state estimation data assimilation
2013/4/28
The Kalman filter is extensively used for state estimation for linear systems under Gaussian noise. When non-Gaussian L\'evy noise is present, the conventional Kalman filter may fail to be effective d...
Top-down particle filtering for Bayesian decision trees
Top-down particle filtering Bayesian decision trees
2013/4/27
Decision tree learning is a popular approach for classification and regression in machine learning and statistics, and Bayesian formulations---which introduce a prior distribution over decision trees,...
Re-Weighted l_1 Dynamic Filtering for Time-Varying Sparse Signal Estimation
Re-Weighted Dynamic Filtering Time-Varying Signal Estimation
2012/9/17
Signal estimation from incomplete observations improves as more signal structure can be exploited in the inference process. Classic algorithms (e.g., Kalman filtering) have exploited strong dynamic st...
A Contextual Item-Based Collaborative Filtering Technology
Context Item-Based Collaborative Filtering
2013/1/28
This paper proposes a contextual item-based collaborative filtering technology, which is based on the traditional item-based collaborative filtering technology. In the process of the recommendation, u...
On absolutely continuous compensators and nonlinear filtering equations in default risk models
Azema supermartingale default indicator absolutely continuous compensators pricing of default risk nonlinear filtering
2012/6/5
We discuss the pricing of defaultable assets in an incomplete information model where the default time is given by a first hitting time of an unobservable process. We show that in a fairly general Mar...