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The design of a good kernel is fundamental for knowledge discovery from graph-structured data. Existing graph kernels exploit only limited information about the graph structures but are still computationally expensive. We propose a novel graph kernel based on the structural characteristics of graphs. The key is to represent node labels as binary arrays and characterize each node using logical operations...
In this paper, we formulate a new research problem of learning from vaguely labeled one-class data streams, where the main objective is to allow users to label instance groups, instead of single instances, as positive samples for learning. The batch-labeling, however, raises serious issues because labeled groups may contain non-positive samples, and users may change their labeling interests at any...
Triggered by a market relevant application that involves making joint predictions of pedestrian and public transit flows in urban areas, we address the question of how to utilize hidden common cause relations among variables of interest in order to improve performance in the two related regression tasks. Specifically, we propose stacked Gaussian process learning, a meta-learning scheme in which a...
Relevance feedback is a good method for the semantic gap between the low-level similarity and the high-level user's query in content-based image retrieval. It interactively asks user whether certain proposed images and the query output are relevant or not. In this paper we propose the use of a support vector machines for conducting effective relevance feedback for trademark retrieval. The algorithm...
Radio frequency identification (RFID) is an advanced tracking technology that can be used to study the spatio-temporal behavior of customers in a supermarket. The aim of this work is to build a new RFID-based autonomous system to follow individuals' spatio-temporal activity, a tool not currently available, and to develop new methods for automatic data mining. Here, we study how to transform these...
A Smith Predictor-like design for compensation of arbitrarily long input delays is available for general, controllable, possibly unstable LTI finite-dimensional systems. Such a design has not been proposed previously for problems where the plant is a PDE. We present a design and stability analysis for a prototype problem, where the plant is a reaction-diffusion (parabolic) PDE, with boundary control...
In this paper we present the design of a decentralized vision-based object search system that can be used for elder care in a smart environment. In our approach, each autonomous search agent maintains separate estimates of the probability density function (PDF) of the object location and makes independent decisions about its search process. Asynchronous cooperative search is achieved by transmitting...
We show how to compute a minimal Riccati-balanced state map and a minimal Riccati-balanced state space representation starting from an image representation of a strictly dissipative system. The result is based on an iterative procedure to solve a generalization of the Nevanlinna interpolation problem.
This paper addresses the variance quantification problem for system identification based on the prediction error framework. The role of input and model class selection for the auto-covariance of the estimated transfer function is explained without reference to any particular parametrization. This is achieved by lifting the concept of covariance from the parameter space to the system manifold where...
The stochastic distribution control (SDC) problem is a generalised form of the minimum variance control problem where non-Gaussian noise distributions are encountered. The problem has been previously solved using two alternative approaches. When it is assumed that the output probability distribution function (PDF) is measurable, then a parameterized controller is obtained. If on the other hand this...
In this paper, we focus on the problem of shape retrieval. A novel approach, called improved graph transduction, is proposed. As preceding graph transduction method, we regard the shape as a node in a graph and the similarity of shapes is represented by the edge of the graph. Then we learn a new distance measure between the query shape and the testing shapes. The main contribution of our work is to...
A solution to evaluate network workload by data fusion is put forward, which can be for surveillance the traffic of interconnected communications network in order to keep the network working well by identifying potentially serious problems in the early stages and evaluating network performance. Through fusing the historic network traffic data and network online traffic data, which is based on least...
Expert finding is the task of identifying persons with expertise on a given topic. Existing methods try to model the dependencies between candidates and terms with distance measure or sequential measure, which have been proven to be effective. However, to the best of our knowledge, no work has been conducted on the combination of the two dependencies. In this paper, we propose a language model based...
Mean shift spectral clustering (MSSC) brings us an alternative for image segmentation. However, owing to being based on the classical Parzen window estimator (PW) and employing the full data sample for density estimation, the usefulness of MSSC is weakened. In this paper, the improved mean shift spectral clustering (IMSSC) algorithm is proposed by replacing PW with the reduced set density estimator...
This paper presents a semi-supervised algorithm for the classification of water regions in SAR images. The proposed technique is based on region based level sets and non-parametric estimation of the probability density function (PDF) of the pixel intensities. The level set framework allows automatic topology adaptation and provides the regularization while the PDF's are estimated in each region using...
In this paper, the texture property ??coarseness?? is modeled by means of type-2 fuzzy sets, relating representative coarseness measures (our reference set) with the human perception of this texture property. The type-2 approach allows to face both the imprecision in the interpretation of the measure value and the uncertainty about the coarseness degree associated to a measure value. In our study,...
Under the linear loss, we consider the test problem of the life parameter in the exponential distribution using empirical Bayes (EB) approach and present a monotone EB test possessing a rate of convergence which can be arbitrarily close to O(n-1) under the condition that the past samples are S\phiS-mixing.
Model based intrusion detection mechanisms have produced encouraging results for reduced false alarms. This paper extends our earlier work, where we reported for sandboxing Linux 2.6 using code generated from policies. Here we pursue the problem of code generation from a set of policies extracted from a domain model. Such a technique can support the safeguarding of system resources. We also present...
An improved particle filter for nonlinear, non-Gaussian estimation is proposed in this paper. The algorithm consists of a particle filter that uses a proximal support vector regression (PSVR) based re-weighting scheme to re-approximate the posterior density and avoid sample impoverishment. A regression function is obtained by PSVR over the weighted sample set and each sample is re-weighted via this...
In this paper, we research a class variational image inpainting models with total variation regularization. Using a splitting technique, an iterative procedure of alternately solving a pair of easy subproblems is constructed. The proposed approach has fast speed than state-of-the-art methods which need to calculate Euler-Lagrange equation. The experiments show that our algorithm visually can obtain...
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