scholarly journals Kernel Representation Approach to Persistence of Behavior

2014 ◽  
Vol 47 (3) ◽  
pp. 6032-6037 ◽  
Author(s):  
Abdul Basit Memon ◽  
Erik I. Verriest
Keyword(s):  
Kernel Representation

A kernel representation for exponential splines with global tension

2009 ◽  
Author(s):  
Sven Barendt ◽  
Bernd Fischer ◽  
Jan Modersitzki
Keyword(s):  
Kernel Representation

On Additive Operators

1971 ◽  
Vol 23 (3) ◽  
pp. 468-480 ◽  
Author(s):  
N. A. Friedman ◽  
A. E. Tong
Keyword(s):  
Banach Space ◽  
Compact Hausdorff Space ◽  
Weak Topology ◽  
Hausdorff Space ◽  
Continuous Functions ◽  
Additive Functional ◽  
Representation Theorems ◽  
Image Position ◽  
Vector Valued ◽  
Kernel Representation

Representation theorems for additive functional have been obtained in [2, 4; 6-8; 10-13]. Our aim in this paper is to study the representation of additive operators.Let S be a compact Hausdorff space and let C(S) be the space of real-valued continuous functions defined on S. Let X be an arbitrary Banach space and let T be an additive operator (see § 2) mapping C(S) into X. We will show (see Lemma 3.4) that additive operators may be represented in terms of a family of “measures” {μh} which take their values in X**. If X is weakly sequentially complete, then {μh} can be shown to take their values in X and are vector-valued measures (i.e., countably additive in the norm) (see Lemma 3.7). And, if X* is separable in the weak-* topology, T may be represented in terms of a kernel representation satisfying the Carathéordory conditions (see [9; 11; §4]):

Automatic Motion Segmentation via a Cumulative Kernel Representation and Spectral Clustering

2017 ◽  
pp. 406-414 ◽  
Author(s):  
O. R. Oña-Rocha ◽  
O. T. Sánchez-Manosalvas ◽  
A. C. Umaquinga-Criollo ◽  
P. D. Rosero-Montalvo ◽  
L. E. Suárez-Zambrano ◽  
...  
Keyword(s):  
Spectral Clustering ◽  
Motion Segmentation ◽  
Kernel Representation

Kernel representation-based nearest neighbor classifier

Optik ◽  
2014 ◽  
Vol 125 (10) ◽  
pp. 2320-2326 ◽  
Author(s):  
Xiaozhao Fang ◽  
Yuwu Lu ◽  
Zhengming Li ◽  
Lei Yu ◽  
Yan Chen
Keyword(s):  
Nearest Neighbor ◽  
Nearest Neighbor Classifier ◽  
Kernel Representation ◽  
Neighbor Classifier

Representations of continuous-time ARMA processes

2004 ◽  
Vol 41 (A) ◽  
pp. 375-382 ◽  
Author(s):  
Peter J. Brockwell
Keyword(s):  
Continuous Time ◽  
Moving Average ◽  
Asymptotic Properties ◽  
Autoregressive Moving Average ◽  
Moving Average Process ◽  
Arma Process ◽  
Simple Modification ◽  
Arma Processes ◽  
Kernel Representation ◽  
Autoregressive Moving Average Process

Using the kernel representation of a continuous-time Lévy-driven ARMA (autoregressive moving average) process, we extend the class of nonnegative Lévy-driven Ornstein–Uhlenbeck processes employed by Barndorff-Nielsen and Shephard (2001) to allow for nonmonotone autocovariance functions. We also consider a class of fractionally integrated Lévy-driven continuous-time ARMA processes obtained by a simple modification of the kernel of the continuous-time ARMA process. Asymptotic properties of the kernel and of the autocovariance function are derived.

Sign in / Sign up

Export Citation Format

Share Document