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Found 31 papers, 1 papers with code
Evidential-EM Algorithm Applied to Progressively Censored Observations
Evidential-EM (E2M) algorithm is an effective approach for computing maximum likelihood estimations under finite mixture models, especially when there is uncertain information about data.
Median evidential c-means algorithm and its application to community detection
In this paper, a new prototype-based clustering method, called Median Evidential C-Means (MECM), which is an extension of median c-means and median fuzzy c-means on the theoretical framework of belief functions is proposed.
Evidential relational clustering using medoids
Medoid-based clustering algorithms, which assume the prototypes of classes are objects, are of great value for partitioning relational data sets.
Adaptive imputation of missing values for incomplete pattern classification
We propose a credal classification method for incomplete pattern with adaptive imputation of missing values based on belief function theory.
The belief noisy-or model applied to network reliability analysis
In this paper, an extension of NOR model based on the theory of belief functions, named Belief Noisy-OR (BNOR), is proposed.
ECMdd: Evidential c-medoids clustering with multiple prototypes
In the application of FCMdd and original ECMdd, a single medoid (prototype), which is supposed to belong to the object set, is utilized to represent one class.
Evidential Label Propagation Algorithm for Graphs
With the increasing size of social networks in real world, community detection approaches should be fast and accurate.
Semi-supervised evidential label propagation algorithm for graph data
In the task of community detection, there often exists some useful prior information.
Joint Target Detection and Tracking in Multipath Environment: A Variational Bayesian Approach
We consider multitarget detection and tracking problem for a class of multipath detection system where one target may generate multiple measurements via multiple propagation paths, and the association relationship among targets, measurements and propagation paths is unknown.
Evidence combination for a large number of sources
It will keep the spirit of the conjunctive rule to reinforce the belief on the focal elements with which the sources are in agreement.
Disentangled Variational Auto-Encoder for Semi-supervised Learning
The majority of existing semi-supervised VAEs utilize a classifier to exploit label information, where the parameters of the classifier are introduced to the VAE.
Evidential community detection based on density peaks
Credal partitions in the framework of belief functions can give us a better understanding of the analyzed data set.
Joint Stereo Video Deblurring, Scene Flow Estimation and Moving Object Segmentation
Under our model, these three tasks are naturally connected and expressed as the parameter estimation of 3D scene structure and camera motion (structure and motion for the dynamic scenes).
OTHR multitarget tracking with a GMRF model of ionospheric parameters
Therefore, to improve the localization accuracy of OTHR, it is important to develop accurate models and estimation methods of ionospheric parameters and the corresponding target tracking algorithms.
EGMM: an Evidential Version of the Gaussian Mixture Model for Clustering
The Gaussian mixture model (GMM) provides a simple yet principled framework for clustering, with properties suitable for statistical inference.
Pose Discrepancy Spatial Transformer Based Feature Disentangling for Partial Aspect Angles SAR Target Recognition
This letter presents a novel framework termed DistSTN for the task of synthetic aperture radar (SAR) automatic target recognition (ATR).
TECM: Transfer Learning-based Evidential C-Means Clustering
As a representative evidential clustering algorithm, evidential c-means (ECM) provides a deeper insight into the data by allowing an object to belong not only to a single class, but also to any subset of a collection of classes, which generalizes the hard, fuzzy, possibilistic, and rough partitions.
Deep-Attack over the Deep Reinforcement Learning
Recent adversarial attack developments have made reinforcement learning more vulnerable, and different approaches exist to deploy attacks against it, where the key is how to choose the right timing of the attack.
Robust Multitarget Tracking in Interference Environments: A Message-Passing Approach
Then, a unified MP algorithm is used to infer the marginal posterior probability distributions of targets, clutter, and data association by splitting the joint probability distribution into a mean-field approximate part and a belief propagation part.
Data Augmentation and Classification of Sea-Land Clutter for Over-the-Horizon Radar Using AC-VAEGAN
Using a dataset of OTHR sea-land clutter, both the quality of the synthetic samples and the performance of data augmentation of AC-VAEGAN are verified.
A Sea-Land Clutter Classification Framework for Over-the-Horizon-Radar Based on Weighted Loss Semi-supervised GAN
The semi-supervised classification performance of WL-SSGAN is evaluated on a sea-land clutter dataset.
Combinatorial-restless-bandit-based Transmitter-Receiver Online Selection for Distributed MIMO Radars With Non-Stationary Channels
We track moving targets with a distributed multiple-input multiple-output (MIMO) radar, for which the transmitters and receivers are appropriately paired and selected with a limited number of radar stations.
Classification-Aided Robust Multiple Target Tracking Using Neural Enhanced Message Passing
We first introduce a novel neural enhanced message passing approach, where the beliefs obtained by the unified message passing are fed into the neural network as additional information.
Non-myopic Beam Scheduling for Multiple Smart Target Tracking in Phased Array Radar Network
We present numerical evidence that the model satisfies sufficient conditions for indexability (existence of the Whittle index) based upon partial conservation laws, and, through extensive simulations, we validate the effectiveness of the proposed policy in different scenarios.
PGN: A perturbation generation network against deep reinforcement learning
Deep reinforcement learning has advanced greatly and applied in many areas.
DOEPatch: Dynamically Optimized Ensemble Model for Adversarial Patches Generation
While ensemble models have proven effective, current research in the field of object detection typically focuses on the simple fusion of the outputs of all models, with limited attention being given to developing general adversarial patches that can function effectively in the physical world.
Arithmetic Average Density Fusion -- Part IV: Distributed Heterogeneous Fusion of RFS and LRFS Filters via Variational Approximation
This paper, the fourth part of a series of papers on the arithmetic average (AA) density fusion approach and its application for target tracking, addresses the intricate challenge of distributed heterogeneous multisensor multitarget tracking, where each inter-connected sensor operates a probability hypothesis density (PHD) filter, a multiple Bernoulli (MB) filter or a labeled MB (LMB) filter and they cooperate with each other via information fusion.
Multisource Semisupervised Adversarial Domain Generalization Network for Cross-Scene Sea-Land Clutter Classification
MSADGN can extract domain-invariant and domain-specific features from one labeled source domain and multiple unlabeled source domains, and then generalize these features to an arbitrary unseen target domain for real-time prediction of sea\textendash land clutter.
An Index Policy Based on Sarsa and Q-learning for Heterogeneous Smart Target Tracking
Each bandit process is associated with a smart target, of which the estimation state evolves according to different discrete dynamic models for different actions - whether or not the target is being tracked.
Flexible Physical Camouflage Generation Based on a Differential Approach
This involves incorporating a specially designed adversarial loss and covert constraint loss to guarantee the adversarial and covert nature of the camouflage in the physical world.
Full-Time Monocular Road Detection Using Zero-Distribution Prior of Angle of Polarization
Zero-distribution prior embodies the zero-distribution of Angle of Polarization (AoP) of a road scene image, which provides a significant contrast between the road and the background.
