Showing 1-25 of 6,609 resultsfor "Index Terms":Sampling Bias
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Based on the Heuristic Bias Method of Efficient Algorithm of RRT - Connect
2023 2nd International Conference on Artificial Intelligence and Computer Information Technology (AICIT)
Year: 2023 | Conference Paper |
Cited by: Papers (1)
Aiming at the problems of Rapidly Exploring Random Tree (RRT) algorithm with a large number of redundant points and slow convergence in the sampling stage, An efficient RRT-Connect algorithm based on Heuristic Bias RRT-Connect (HB-RRT-Connect) is proposed. The algorithm divides the sampling process into global random sampling and biased target sampling, and determines the next sampling method by j...Show More
Sample Alignment for Image-to-Image Translation Based Medical Domain Adaptation
Heng Li;Haofeng Liu;Xiaoxuan Wang;Chenlang Yi;Hao Chen;Yan Hu;Jiang Liu
2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI)
Year: 2022 | Conference Paper |
Image-to-image (I2I) translation is a popular paradigm in domain adaptation (DA), and has been frequently used to address the lack of labeled data. However, as a result of the sample bias in medical data caused by the attributes of imaging modality or pathology, the I2I translation based DA always suffers from synthesis artifacts. For boosting the DA in medical scenarios, a sample alignment algori...Show More
Practical Uniform Peer Sampling under Churn
Roberto Baldoni;Marco Platania;Leonardo Querzoni;Sirio Scipioni
2010 Ninth International Symposium on Parallel and Distributed Computing
Year: 2010 | Conference Paper |
Cited by: Papers (3)
Providing independent uniform samples from a system population poses considerable problems in highly dynamic settings, like P2P systems, where the number of participants and their unpredictable behavior (e.g., churn, crashes etc.) may introduce relevant bias. Current implementations of the Peer Sampling Service are designed to provide uniform samples only in static settings and do not consider tha...Show More
Effect Inference From Two-Group Data With Sampling Bias
IEEE Signal Processing Letters
Cited by: Papers (2)
In many applications, different populations are compared using data that are sampled in a biased manner. Under sampling biases, standard methods that estimate the difference between the population means yield unreliable inferences. Here, we develop an inference method that is resilient to sampling biases and is able to control the false positive errors under moderate bias levels in contrast to the...Show More
Study of sampling techniques and algorithms in data stream environments
2012 9th International Conference on Fuzzy Systems and Knowledge Discovery
Year: 2012 | Conference Paper |
Cited by: Papers (10)
Sampling is the most versatile approximation technique available and is still one of the most powerful methods for building a one-pass synopsis of a data set in a streaming environment. Throughout the detailed review, a kind of taxonomic frame of sampling algorithms was presented; meanwhile, discussions and comparisons of representative sampling algorithms were performed. Due to the limitations of...Show More
Bias correction in clustering coefficient estimation
2017 IEEE International Conference on Big Data (Big Data)
Year: 2017 | Conference Paper |
Cited by: Papers (2)
Clustering coefficient (C) is an important structural property to understand the complex structure of a graph. Calculating C is a computationally intensive task. Thereby, sampling-based methods have attracted substantial research for estimating C, and the closely related metric, the number of triangles. Unfortunately, widely used estimators for C are biased. We quantify the bias using Taylor expan...Show More
Active and Semisupervised Learning for the Classification of Remote Sensing Images
This paper aims at analyzing and comparing active learning (AL) and semisupervised learning (SSL) methods for the classification of remote sensing (RS) images. We present a literature review of the two learning paradigms and compare them theoretically and experimentally when addressing classification problems characterized by few training samples (w.r.t. the number of features) and affected by sam...Show More
Learning Content and Context with Language Bias for Visual Question Answering
Chao Yang;Su Feng;Dongsheng Li;Huawei Shen;Guoqing Wang;Bin Jiang
2021 IEEE International Conference on Multimedia and Expo (ICME)
Year: 2021 | Conference Paper |
Cited by: Papers (12)
Visual Question Answering (VQA) is a challenging multi-modal task to answer questions about an image. Many works concentrate on how to reduce language bias which makes models answer questions ignoring visual content and language context. However, reducing language bias also weakens the ability of VQA models to learn context prior. To address this issue, we propose a novel learning strategy named C...Show More
Semi-Supervised Fisher Linear Discriminant (SFLD)
2010 IEEE International Conference on Acoustics, Speech and Signal Processing
Year: 2010 | Conference Paper |
Cited by: Papers (3)
Supervised learning uses a training set of labeled examples to compute a classifier which is a mapping from feature vectors to class labels. The success of a learning algorithm is evaluated by its ability to generalize, i.e., to extend this mapping accurately to new data that is commonly referred to as the test data. Good generalization depends crucially on the quality of the training set. Because...Show More
Using Biased Social Samples for Disaster Response: Extended Abstract
2016 IEEE 2nd International Conference on Collaboration and Internet Computing (CIC)
Year: 2016 | Conference Paper |
Social media is an important data source. Every day, billions of posts, likes, and connections are created by people around the globe. By monitoring social media platforms, we can observe important topics, as well as find new topics of discussion as they emerge. This is never more apparent than in disaster scenarios, where people post in real-time about what is unfolding on the ground. Social medi...Show More
Threshold Sampling for Network Streaming Data Analysis
2008 International Conference on Advanced Computer Theory and Engineering
Year: 2008 | Conference Paper |
Cited by: Papers (1)
Network streaming data are the network traffic records coming from high-speed network links. They arrive continually and their volumes are huge. The key to analysis of network streaming data is to design a smaller yet well organized data subset to glean the most important information for quickly answering a specific type of query. In this paper, we propose a threshold sampling algorithm for networ...Show More
Graph sampling: Estimation of degree distributions
2013 IEEE International Conference on Acoustics, Speech and Signal Processing
Year: 2013 | Conference Paper |
Cited by: Papers (4)
Online social networks and the World Wide Web lead to large underlying graphs that might not be completely known because of their size. To compute reliable statistics, we have to resort to sampling the network. In this paper, we investigate four network sampling methods to estimate the network degree distribution and the so-called biased degree distribution of a 3.7 million wireless subscriber net...Show More
Is Heuristic Sampling Necessary in Training Deep Object Detectors?
Joya Chen;Dong Liu;Tong Xu;Shiwei Wu;Yifei Cheng;Enhong Chen
To train accurate deep object detectors under the extreme foreground-background imbalance, heuristic sampling methods are always necessary, which either re-sample a subset of all training samples (hard sampling methods, e.g. biased sampling, OHEM), or use all training samples but re-weight them discriminatively (soft sampling methods, e.g. Focal Loss, GHM). In this paper, we challenge the necessit...Show More
Bias Correction in a Small Sample from Big Data
This paper discusses the bias problem when estimating the population size of big data such as online social networks (OSN) using uniform random sampling and simple random walk. Unlike the traditional estimation problem where the sample size is not very small relative to the data size, in big data, a small sample relative to the data size is already very large and costly to obtain. We point out tha...Show More
Consistent Estimation of Non-bandlimited Spectral Density From Uniformly Spaced Samples
In the matter of selection of sample time points for the estimation of the power spectral density of a continuous time stationary stochastic process, irregular sampling schemes such as Poisson sampling are often preferred over regular (uniform) sampling. A major reason for this preference is the well-known problem of inconsistency of estimators based on regular sampling, when the underlying power ...Show More
IT-Enabled Survey and Its Problems: Analyses of an Australian Internet Poll
2009 Ninth Annual International Symposium on Applications and the Internet
Year: 2009 | Conference Paper |
The use of web-based surveys has expanded rapidly during the past few years as the number of Internet users increased. However, lack of representativeness cause hesitation in utilizing data collected via the Internet as compared to more conventional sampling methodology. This paper analyses the problem of non-representativeness in open online surveys using a passive sampling method, taking as an e...Show More
A Measurable Framework for Run-time Data Sampling in Large-scale Datacenter
2019 IEEE International Conference on Signal, Information and Data Processing (ICSIDP)
Year: 2019 | Conference Paper |
In large-scale data center, collecting run-time data is a very effective method which can be used to analyze and monitor the performance of data centers. But due to the huge size of data centers, limited computing resources and the requirement of low delay, it is very difficult and unrealistic to collect all the data in large-scale data centers. Therefore, to solve the serious problem, sampling pa...Show More
Switching edges to randomize networks: what goes wrong and how to fix it
The switching model is a well-known random network model that randomizes a network while keeping its degree sequence fixed. The idea behind the switching model is simple: a network is randomized by repeatedly rewiring pairs of edges. In this paper we demonstrate that despite its simple description, and in part due to it, much can go wrong when implementing the switching model. Specifically, we sho...Show More
On the Coverage Probability of Bias-Corrected Confidence Bounds
2020 Asia-Pacific International Symposium on Advanced Reliability and Maintenance Modeling (APARM)
Year: 2020 | Conference Paper |
Cited by: Papers (1)
In this paper, we conduct Monte Carlo simulation studies in order to assess the coverage probability of bias-corrected confidence bounds for the Weibull distribution. It is shown that coverage probabilities of confidence bounds are highly dependent on the chosen bias-correction method. Based on simulation results, recommendations for choosing the best performing combinations of bias-corrections an...Show More
Toward an Enhanced SMOS Level-2 Ocean Salinity Product
Estrella Olmedo;Verónica González-Gambau;Antonio Turiel;Sébastien Guimbard;Cristina Gonźalez-Haro;Justino Martínez;Carolina Gabarró;Marcos Portabella;Manuel Arias;Roberto Sabia;Roger Oliva;Ignasi Corbella
The quality of the Soil Moisture and Ocean Salinity (SMOS) sea surface salinity (SSS) measurements has been noticeably improved in the past years. However, for some applications, there are still some limitations in the use of the Level-2 ocean salinity product. First, the SSS measurements are still affected by a latitudinal and seasonal bias. Second, the high standard deviation of the SSS error co...Show More
Performance analysis of cart and C5.0 using sampling techniques
2016 IEEE International Conference on Advances in Computer Applications (ICACA)
Year: 2016 | Conference Paper |
Cited by: Papers (10)
Data mining is the process of extracting the hidden predictive model from large databases. It has various methods and algorithms. Classification is a supervised method, which builds a model for predicting the new instances. Different algorithms like decision tree, neural networks, support vector machines, k nearest neighbour, Bayesian classification are available for the classification. Decision t...Show More
Ensemble Direct Density Ratio Estimation for Multistream Classification
Swarup Chandra;Ahsanul Haque;Hemeng Tao;Jie Liu;Latifur Khan;Charu Aggarwal
2018 IEEE 34th International Conference on Data Engineering (ICDE)
Year: 2018 | Conference Paper |
Cited by: Papers (6)
In traditional machine learning, it is assumed that training data conforming to the stationary distribution of test data is readily available. Yet, such an assumption is not valid in practice due to a high cost of obtaining the truth value of data instances. This is particularly true when computing over non-stationary data streams. Recent studies in the multistream setting aim to address this issu...Show More
Multi Task Learning: A Survey and Future Directions
2023 International Conference on Artificial Intelligence in Information and Communication (ICAIIC)
Year: 2023 | Conference Paper |
Cited by: Papers (3)
Multi-task learning (MTL) is a problem that must be applied in modern recommendation systems and is just as difficult. In the recent e-commerce advertising market, it is necessary to be able to predict not only the probability of users clicking, but also the probability of conversion and purchase. By predicting multi-task, it is possible to increase the accuracy of each task and optimize advertise...Show More
Efficiently Estimating Statistics of Points of Interests on Maps
Recently, map services (e.g., Google maps) and location-based online social networks (e.g., Foursquare) attract a lot of attention and businesses. With the increasing popularity of these location-based services, exploring and characterizing points of interests (PoIs) such as restaurants and hotels on maps provides valuable information for applications such as start-up marketing research. Due to th...Show More
In recent years, deep learning has developed rapidly and achieved great success in many fields. However, it has been demonstrated that deep neural networks are very vulnerable to artificially designed adversarial examples which are difficult to visually observe by human. In this paper, the practical hard-label black box attack in which attackers can only query the output labels to generate adversa...Show More
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