Relevant bibliographies by topics / Neighbor selection

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Neighbor selection'

Author: Grafiati
Published: 4 June 2021
Last updated: 5 February 2022

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Journal articles on the topic "Neighbor selection"

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Wang, Bingming, Shi Ying, and Zhe Yang. "A Log-Based Anomaly Detection Method with Efficient Neighbor Searching and Automatic K Neighbor Selection." Scientific Programming 2020 (June 2, 2020): 1–17. http://dx.doi.org/10.1155/2020/4365356.

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Using the k-nearest neighbor (kNN) algorithm in the supervised learning method to detect anomalies can get more accurate results. However, when using kNN algorithm to detect anomaly, it is inefficient at finding k neighbors from large-scale log data; at the same time, log data are imbalanced in quantity, so it is a challenge to select proper k neighbors for different data distributions. In this paper, we propose a log-based anomaly detection method with efficient selection of neighbors and automatic selection of k neighbors. First, we propose a neighbor search method based on minhash and MVP-tree. The minhash algorithm is used to group similar logs into the same bucket, and MVP-tree model is built for samples in each bucket. In this way, we can reduce the effort of distance calculation and the number of neighbor samples that need to be compared, so as to improve the efficiency of finding neighbors. In the process of selecting k neighbors, we propose an automatic method based on the Silhouette Coefficient, which can select proper k neighbors to improve the accuracy of anomaly detection. Our method is verified on six different types of log data to prove its universality and feasibility.
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Zhai, Junhai, Jiaxing Qi, and Sufang Zhang. "An instance selection algorithm for fuzzy K-nearest neighbor." Journal of Intelligent & Fuzzy Systems 40, no. 1 (January 4, 2021): 521–33. http://dx.doi.org/10.3233/jifs-200124.

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The condensed nearest neighbor (CNN) is a pioneering instance selection algorithm for 1-nearest neighbor. Many variants of CNN for K-nearest neighbor have been proposed by different researchers. However, few studies were conducted on condensed fuzzy K-nearest neighbor. In this paper, we present a condensed fuzzy K-nearest neighbor (CFKNN) algorithm that starts from an initial instance set S and iteratively selects informative instances from training set T, moving them from T to S. Specifically, CFKNN consists of three steps. First, for each instance x ∈ T, it finds the K-nearest neighbors in S and calculates the fuzzy membership degrees of the K nearest neighbors using S rather than T. Second it computes the fuzzy membership degrees of x using the fuzzy K-nearest neighbor algorithm. Finally, it calculates the information entropy of x and selects an instance according to the calculated value. Extensive experiments on 11 datasets are conducted to compare CFKNN with four state-of-the-art algorithms (CNN, edited nearest neighbor (ENN), Tomeklinks, and OneSidedSelection) regarding the number of selected instances, the testing accuracy, and the compression ratio. The experimental results show that CFKNN provides excellent performance and outperforms the other four algorithms.
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Liu, Huawen, Xindong Wu, and Shichao Zhang. "Neighbor selection for multilabel classification." Neurocomputing 182 (March 2016): 187–96. http://dx.doi.org/10.1016/j.neucom.2015.12.035.

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Sironen, S., A. Kangas, M. Maltamo, and J. Kangas. "Estimating individual tree growth with nonparametric methods." Canadian Journal of Forest Research 33, no. 3 (March 1, 2003): 444–49. http://dx.doi.org/10.1139/x02-162.

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The aim of the study was to demonstrate the use of nonparametric methods in estimating tree-level growth models. In the nonparametric methods the growth of a tree is predicted as a weighted mean of the values of neighboring observations. The selection of the nearest neighbors is based on the similarities between tree- and stand-level characteristics of the target tree and the neighbors. The data for the models were collected from Kuusamo in northeastern Finland. Models for the 5-year diameter growth were constructed for Scots pine (Pinus sylvestris L.) with three different nonparametric methods: the k-nearest neighbor regression, k-most-similar neighbor, and generalized additive model.
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Pfahlberg, A., O. Gefeller, and R. Weißbach. "Double-smoothing in Kernel Hazard Rate Estimation." Methods of Information in Medicine 47, no. 02 (2008): 167–73. http://dx.doi.org/10.3414/me0447.

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Summary Objectives: In oncological studies, the hazard rate can be used to differentiate subgroups of the study population according to their patterns of survival risk over time. Nonparametric curve estimation has been suggested as an exploratory means of revealing such patterns. The decision about the type of smoothing parameter is critical for performance in practice. In this paper, we study data-adaptive smoothing. Methods: A decade ago, the nearest-neighbor bandwidth was introduced for censored data in survival analysis. It is specified by one parameter, namely the number of nearest neighbors. Bandwidth selection in this setting has rarely been investigated, although the heuristical advantages over the frequently-studied fixed bandwidth are quite obvious. The asymptotical relationship between the fixed and the nearest-neighbor bandwidth can be used to generate novel approaches. Results: We develop a new selection algorithm termed double-smoothing for the nearest-neighbor bandwidth in hazard rate estimation. Our approach uses a finite sample approximation of the asymptotical relationship between the fixed and nearest-neighbor bandwidth. By so doing, we identify the nearest-neighbor bandwidth as an additional smoothing step and achieve further data-adaption after fixed bandwidth smoothing. We illustrate the application of the new algorithm in a clinical study and compare the outcome to the traditional fixed bandwidth result, thus demonstrating the practical performance of the technique. Conclusion: The double-smoothing approach enlarges the methodological repertoire for selecting smoothing parameters in nonparametric hazard rate estimation. The slight increase in computational effort is rewarded with a substantial amount of estimation stability, thus demonstrating the benefit of the technique for biostatistical applications.
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Jagruthi, Y., Dr Y. Ramadevi, and A. Sangeeta. "An Instance Selection Algorithm Based On Reverse k Nearest Neighbor." INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 10, no. 7 (August 30, 2013): 1858–61. http://dx.doi.org/10.24297/ijct.v10i7.3217.

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Classification is one of the most important data mining techniques. It belongs to supervised learning. The objective of classification is to assign class label to unlabelled data. As data is growing rapidly, handling it has become a major concern. So preprocessing should be done before classification and hence data reduction is essential. Data reduction is to extract a subset of features from a set of features of a data set. Data reduction helps in decreasing the storage requirement and increases the efficiency of classification. A way to measure data reduction is reduction rate. The main thing here is choosing representative samples to the final data set. There are many instance selection algorithms which are based on nearest neighbor decision rule (NN). These algorithms select samples on incremental strategy or decremental strategy. Both the incremental algorithms and decremental algorithms take much processing time as they iteratively scan the dataset. There is another instance selection algorithm, reverse nearest neighbor reduction (RNNR) based on the concept of reverse nearest neighbor (RNN). RNNR does not iteratively scan the data set. In this paper, we extend the RNN to RkNN and we use the concept of RNNR to RkNN. RkNN finds the query objects that has the query point as their k nearest-neighbors. Our approach utilizes the advantage of RNN and proposes to use the concept of RkNN. We have taken the dataset of theatres, hospitals and restaurants and extracted the sample set. Classification has been done the resultant sample data set. We observe two parameters here they are classification accuracy and reduction rate.
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Cano, José-Ramón, Naif R. Aljohani, Rabeeh Ayaz Abbasi, Jalal S. Alowidbi, and Salvador García. "Prototype selection to improve monotonic nearest neighbor." Engineering Applications of Artificial Intelligence 60 (April 2017): 128–35. http://dx.doi.org/10.1016/j.engappai.2017.02.006.

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Liu, Xianglong, Junfeng He, and Shih-Fu Chang. "Hash Bit Selection for Nearest Neighbor Search." IEEE Transactions on Image Processing 26, no. 11 (November 2017): 5367–80. http://dx.doi.org/10.1109/tip.2017.2695895.

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Zhang, Shichao. "Nearest neighbor selection for iteratively kNN imputation." Journal of Systems and Software 85, no. 11 (November 2012): 2541–52. http://dx.doi.org/10.1016/j.jss.2012.05.073.

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Cafagna, Francesco, Michael H. Böhlen, and Annelies Bracher. "Category- and selection-enabled nearest neighbor joins." Information Systems 68 (August 2017): 3–16. http://dx.doi.org/10.1016/j.is.2017.01.006.

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Dissertations / Theses on the topic "Neighbor selection"

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Woerner, August Eric, and August Eric Woerner. "On the Neutralome of Great Apes and Nearest Neighbor Search in Metric Spaces." Diss., The University of Arizona, 2016. http://hdl.handle.net/10150/621578.

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Problems of population genetics are magnified by problems of big data. My dissertation spans the disciplines of computer science and population genetics, leveraging computational approaches to biological problems to address issues in genomics research. In this dissertation I develop more efficient metric search algorithms. I also show that vast majority of the genomes of great apes are impacted by the forces of natural selection. Finally, I introduce a heuristic to identify neutralomes—regions that are evolving with minimal selective pressures—and use these neutralomes for inferences on effective population size in great apes. We begin with a formal and far-reaching problem that impacts a broad array of disciplines including biology and computer science; the 𝑘-nearest neighbors problem in generalized metric spaces. The 𝑘-nearest neighbors (𝑘-NN) problem is deceptively simple. The problem is as follows: given a query q and dataset D of size 𝑛, find the 𝑘-closest points to q. This problem can be easily solved by algorithms that compute 𝑘th order statistics in O(𝑛) time and space. It follows that if D can be ordered, then it is perhaps possible to solve 𝑘-NN queries in sublinear time. While this is not possible for an arbitrary distance function on the points in D, I show that if the points are constrained by the triangle inequality (such as with metric spaces), then the dataset can be properly organized into a dispersion tree (Appendix A). Dispersion trees are a hierarchical data structure that is built around a large dispersed set of points. Dispersion trees have construction times that are sub-quadratic (O(𝑛¹·⁵ log⁡ 𝑛)) and use O(𝑛) space, and they use a provably optimal search strategy that minimizes the number of times the distance function is invoked. While all metric data structures have worst-case O(𝑛) search times, dispersion trees have average-case search times that are substantially faster than a large sampling of comparable data structures in the vast majority of spaces sampled. Exceptions to this include extremely high dimensional space (d>20) which devolve into near-linear scans of the dataset, and unstructured low-dimensional (d<6) Euclidean spaces. Dispersion trees have empirical search times that appear to scale as O(𝑛ᶜ) for 0
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Bengtsson, Thomas. "Time series discrimination, signal comparison testing, and model selection in the state-space framework /." free to MU campus, to others for purchase, 2000. http://wwwlib.umi.com/cr/mo/fullcit?p9974611.

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Karginova, Nadezda. "Identification of Driving Styles in Buses." Thesis, Halmstad University, Intelligent systems (IS-lab), 2010. http://urn.kb.se/resolve?urn=urn:nbn:se:hh:diva-4830.

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It is important to detect faults in bus details at an early stage. Because the driving style affects the breakdown of different details in the bus, identification of the driving style is important to minimize the number of failures in buses.

The identification of the driving style of the driver was based on the input data which contained examples of the driving runs of each class. K-nearest neighbor and neural networks algorithms were used. Different models were tested.

It was shown that the results depend on the selected driving runs. A hypothesis was suggested that the examples from different driving runs have different parameters which affect the results of the classification.

The best results were achieved by using a subset of variables chosen with help of the forward feature selection procedure. The percent of correct classifications is about 89-90 % for the k-nearest neighbor algorithm and 88-93 % for the neural networks.

Feature selection allowed a significant improvement in the results of the k-nearest neighbor algorithm and in the results of the neural networks algorithm received for the case when the training and testing data sets were selected from the different driving runs. On the other hand, feature selection did not affect the results received with the neural networks for the case when the training and testing data sets were selected from the same driving runs.

Another way to improve the results is to use smoothing. Computing the average class among a number of consequent examples allowed achieving a decrease in the error.

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FAIRBANKS, MICHAEL STEWART. "MINIMIZING CONGESTION IN PEER-TO-PEER NETWORKS UNDER THE PRESENCE OF GUARDED NODES." University of Cincinnati / OhioLINK, 2006. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1147362818.

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Dong, Yingying. "Microeconometric Models with Endogeneity -- Theoretical and Empirical Studies." Thesis, Boston College, 2009. http://hdl.handle.net/2345/753.

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Thesis advisor: Arthur Lewbel
This dissertation consists of three independent essays in applied microeconomics and econometrics. Essay 1 investigates the issue why individuals with health insurance use more health care. One obvious reason is that health care is cheaper for the insured. But additionally, having insurance can encourage unhealthy behavior via moral hazard. The effect of health insurance on medical utilization has been extensively studied; however, previous work has mostly ignored the effect of insurance on behavior and how that in turn affects medical utilization. This essay examines these distinct effects. The increased medical utilization due to reduced prices may help the insured maintain good health, while that due to increased unhealthy behavior does not, so distinguishing these two effects has important policy implications. A two-period dynamic forward-looking model is constructed to derive the structural causal relationships among the decision to buy insurance, health behaviors (drinking, smoking, and exercise), and medical utilization. The model shows how exogenous changes in insurance prices and past behaviors can identify the direct and indirect effects of insurance on medical utilization. An empirical analysis also distinguishes between intensive and extensive margins (e.g., changes in the number of drinkers vs. the amount of alcohol consumed) of the insurance effect, which turns out to be empirically important. Health insurance is found to encourage less healthy behavior, particularly heavy drinking, but this does not yield a short term perceptible increase in doctor or hospital visits. The effects of health insurance are primarily found at the intensive margin, e.g., health insurance may not cause a non-drinker to take up drinking, while it encourages a heavy drinker to drink even more. These results suggest that to counteract behavioral moral hazard, health insurance should be coupled with incentives that target individuals who currently engage in unhealthy behaviors, such as heavy drinkers. Essay 2 examines the effect of repeating kindergarten on the retained children's academic performance. Although most existing research concludes that grade retention generates no benefits for retainees' later academic performance, holding low achieving children back has been a popular practice for decades. Drawing on a recently collected nationally representative data set in the US, this paper estimates the causal effect of kindergarten retention on the retained children's later academic performance. Since children are observed being held back only when they enroll in schools that permit retention, this paper jointly models 1) the decision of entering a school allowing for kindergarten retention, 2) the decision of undergoing a retention treatment in kindergarten, and 3) children's academic performance in higher grades. The retention treatment is modeled as a binary choice with sample selection. The outcome equations are linear regressions including the kindergarten retention dummy as an endogenous regressor with a correlated random coefficient. A control function estimator is developed for estimating the resulting double-hurdle treatment model, which allows for unobserved heterogeneity in the retention effect. As a comparison, a nonparametric bias-corrected nearest neighbor matching estimator is also implemented. Holding children back in kindergarten is found to have positive but diminishing effects on their academic performance up to the third grade. Essay 3 proves the semiparametric identification of a binary choice model having an endogenous regressor without relying on outside instruments. A simple estimator and a test for endogeneity are provided based on this identification. These results are applied to analyze working age male's migration within the US, where labor income is potentially endogenous. Identification relies on the fact that the migration probability among workers is close to linear in age while labor income is nonlinear in age(when both are nonparametrically estimated). Using data from the PSID, this study finds that labor income is endogenous and that ignoring this endogeneity leads to downward bias in the estimated effect of labor income on the migration probability
Thesis (PhD) — Boston College, 2009
Submitted to: Boston College. Graduate School of Arts and Sciences
Discipline: Economics
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Gopal, Kreshna. "Efficient case-based reasoning through feature weighting, and its application in protein crystallography." [College Station, Tex. : Texas A&M University, 2007. http://hdl.handle.net/1969.1/ETD-TAMU-1906.

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Gashler, Michael S. "Advancing the Effectiveness of Non-Linear Dimensionality Reduction Techniques." BYU ScholarsArchive, 2012. https://scholarsarchive.byu.edu/etd/3216.

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Data that is represented with high dimensionality presents a computational complexity challenge for many existing algorithms. Limiting dimensionality by discarding attributes is sometimes a poor solution to this problem because significant high-level concepts may be encoded in the data across many or all of the attributes. Non-linear dimensionality reduction (NLDR) techniques have been successful with many problems at minimizing dimensionality while preserving intrinsic high-level concepts that are encoded with varying combinations of attributes. Unfortunately, many challenges remain with existing NLDR techniques, including excessive computational requirements, an inability to benefit from prior knowledge, and an inability to handle certain difficult conditions that occur in data with many real-world problems. Further, certain practical factors have limited advancement in NLDR, such as a lack of clarity regarding suitable applications for NLDR, and a general inavailability of efficient implementations of complex algorithms. This dissertation presents a collection of papers that advance the state of NLDR in each of these areas. Contributions of this dissertation include: • An NLDR algorithm, called Manifold Sculpting, that optimizes its solution using graduated optimization. This approach enables it to obtain better results than methods that only optimize an approximate problem. Additionally, Manifold Sculpting can benefit from prior knowledge about the problem. • An intelligent neighbor-finding technique called SAFFRON that improves the breadth of problems that existing NLDR techniques can handle. • A neighborhood refinement technique called CycleCut that further increases the robustness of existing NLDR techniques, and that can work in conjunction with SAFFRON to solve difficult problems. • Demonstrations of specific applications for NLDR techniques, including the estimation of state within dynamical systems, training of recurrent neural networks, and imputing missing values in data. • An open source toolkit containing each of the techniques described in this dissertation, as well as several existing NLDR algorithms, and other useful machine learning methods.
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Holsbach, Nicole. "Método de mineração de dados para diagnóstico de câncer de mama baseado na seleção de variáveis." reponame:Biblioteca Digital de Teses e Dissertações da UFRGS, 2012. http://hdl.handle.net/10183/76183.

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A presente dissertação propõe métodos para mineração de dados para diagnóstico de câncer de mama (CM) baseado na seleção de variáveis. Partindo-se de uma revisão sistemática, sugere-se um método para a seleção de variáveis para classificação das observações (pacientes) em duas classes de resultado, benigno ou maligno, baseado na análise citopatológica de amostras de célula da mama de pacientes. O método de seleção de variáveis para categorização das observações baseia-se em 4 passos operacionais: (i) dividir o banco de dados original em porções de treino e de teste, e aplicar a ACP (Análise de Componentes Principais) na porção de treino; (ii) gerar índices de importância das variáveis baseados nos pesos da ACP e na percentagem da variância explicada pelos componentes retidos; (iii) classificar a porção de treino utilizando as técnicas KVP (k-vizinhos mais próximos) ou AD (Análise Discriminante). Em seguida eliminar a variável com o menor índice de importância, classificar o banco de dados novamente e calcular a acurácia de classificação; continuar tal processo iterativo até restar uma variável; e (iv) selecionar o subgrupo de variáveis responsável pela máxima acurácia de classificação e classificar a porção de teste utilizando tais variáveis. Quando aplicado ao WBCD (Wisconsin Breast Cancer Database), o método proposto apresentou acurácia média de 97,77%, retendo uma média de 5,8 variáveis. Uma variação do método é proposta, utilizando quatro diferentes tipos de kernels polinomiais para remapear o banco de dados original; os passos (i) a (iv) acima descritos são então aplicados aos kernels propostos. Ao aplicar-se a variação do método ao WBCD, obteve-se acurácia média de 98,09%, retendo uma média de 17,24 variáveis de um total de 54 variáveis geradas pelo kernel polinomial recomendado. O método proposto pode auxiliar o médico na elaboração do diagnóstico, selecionando um menor número de variáveis (envolvidas na tomada de decisão) com a maior acurácia, obtendo assim o maior acerto possível.
This dissertation presents a data mining method for breast cancer (BC) diagnosis based on selected features. We first carried out a systematic literature review, and then suggested a method for feature selection and classification of observations, i.e., patients, into benign or malignant classes based on patients’ breast tissue measures. The proposed method relies on four operational steps: (i) split the original dataset into training and testing sets and apply PCA (Principal Component Analysis) on the training set; (ii) generate attribute importance indices based on PCA weights and percent of variance explained by the retained components; (iii) classify the training set using KNN (k-Nearest Neighbor) or DA (Discriminant Analysis) techniques, eliminate irrelevant features and compute the classification accuracy. Next, eliminate the feature with the lowest importance index, classify the dataset, and re-compute the accuracy. Continue such iterative process until one feature is left; and (iv) choose the subset of features yielding the maximum classification accuracy, and classify the testing set based on those features. When applied to the WBCD (Wisconsin Breast Cancer Database), the proposed method led to average 97.77% accurate classifications while retaining average 5.8 features. One variation of the proposed method is presented based on four different types of polynomial kernels aimed at remapping the original database; steps (i) to (iv) are then applied to such kernels. When applied to the WBCD, the proposed modification increased average accuracy to 98.09% while retaining average of 17.24 features from the 54 variables generated by the recommended kernel. The proposed method can assist the physician in making the diagnosis, selecting a smaller number of variables (involved in the decision-making) with greater accuracy, thereby obtaining the highest possible accuracy.
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Ferrero, Carlos Andres. "Algoritmo kNN para previsão de dados temporais: funções de previsão e critérios de seleção de vizinhos próximos aplicados a variáveis ambientais em limnologia." Universidade de São Paulo, 2009. http://www.teses.usp.br/teses/disponiveis/55/55134/tde-19052009-135128/.

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A análise de dados contendo informações sequenciais é um problema de crescente interesse devido à grande quantidade de informação que é gerada, entre outros, em processos de monitoramento. As séries temporais são um dos tipos mais comuns de dados sequenciais e consistem em observações ao longo do tempo. O algoritmo k-Nearest Neighbor - Time Series Prediction kNN-TSP é um método de previsão de dados temporais. A principal vantagem do algoritmo é a sua simplicidade, e a sua aplicabilidade na análise de séries temporais não-lineares e na previsão de comportamentos sazonais. Entretanto, ainda que ele frequentemente encontre as melhores previsões para séries temporais parcialmente periódicas, várias questões relacionadas com a determinação de seus parâmetros continuam em aberto. Este trabalho, foca-se em dois desses parâmetros, relacionados com a seleção de vizinhos mais próximos e a função de previsão. Para isso, é proposta uma abordagem simples para selecionar vizinhos mais próximos que considera a similaridade e a distância temporal de modo a selecionar os padrões mais similares e mais recentes. Também é proposta uma função de previsão que tem a propriedade de manter bom desempenho na presença de padrões em níveis diferentes da série temporal. Esses parâmetros foram avaliados empiricamente utilizando várias séries temporais, inclusive caóticas, bem como séries temporais reais referentes a variáveis ambientais do reservatório de Itaipu, disponibilizadas pela Itaipu Binacional. Três variáveis limnológicas fortemente correlacionadas são consideradas nos experimentos de previsão: temperatura da água, temperatura do ar e oxigênio dissolvido. Uma análise de correlação é realizada para verificar se os dados previstos mantem a correlação das variáveis. Os resultados mostram que, o critério de seleção de vizinhos próximos e a função de previsão, propostos neste trabalho, são promissores
Treating data that contains sequential information is an important problem that arises during the data mining process. Time series constitute a popular class of sequential data, where records are indexed by time. The k-Nearest Neighbor - Time Series Prediction kNN-TSP method is an approximator for time series prediction problems. The main advantage of this approximator is its simplicity, and is often used in nonlinear time series analysis for prediction of seasonal time series. Although kNN-TSP often finds the best fit for nearly periodic time series forecasting, some problems related to how to determine its parameters still remain. In this work, we focus in two of these parameters: the determination of the nearest neighbours and the prediction function. To this end, we propose a simple approach to select the nearest neighbours, where time is indirectly taken into account by the similarity measure, and a prediction function which is not disturbed in the presence of patterns at different levels of the time series. Both parameters were empirically evaluated on several artificial time series, including chaotic time series, as well as on a real time series related to several environmental variables from the Itaipu reservoir, made available by Itaipu Binacional. Three of the most correlated limnological variables were considered in the experiments carried out on the real time series: water temperature, air temperature and dissolved oxygen. Analyses of correlation were also accomplished to verify if the predicted variables values maintain similar correlation as the original ones. Results show that both proposals, the one related to the determination of the nearest neighbours as well as the one related to the prediction function, are promising
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Glawing, Henrik. "Measurement data selection and association in a collision mitigation system." Thesis, Linköping University, Department of Electrical Engineering, 2002. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-1233.

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Today many car manufactures are developing systems that help the driver to avoid collisions. Examples of this kind of systems are: adaptive cruise control, collision warning and collision mitigation / avoidance.

All these systems need to track and predict future positions of surrounding objects (vehicles ahead of the system host vehicle), to calculate the risk of a future collision. To validate that a prediction is correct the predictions must be correlated to observations. This is called the data association problem. If a prediction can be correlated to an observation, this observation is used for updating the tracking filter. This process maintains the low uncertainty level for the track.

From the work behind this thesis, it has been found that a sequential nearest- neighbour approach for the solution of the problem to correlate an observation to a prediction can be used to find the solution to the data association problem.

Since the computational power for the collision mitigation system is limited, only the most dangerous surrounding objects can be tracked and predicted. Therefore, an algorithm that classifies and selects the most critical measurements is developed. The classification into order of potential risk can be done using the measurements that come from an observed object.

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Books on the topic "Neighbor selection"

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Babar, Zahra. Working for the Neighbours. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780190608873.003.0002.

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The forces and factors driving regional migration have become more complex over time, and traditional explanations for the motivations, attraction, and selection of migrants are no longer sufficient in the study of migration to the Persian Gulf. Qatar, which in the last decade has emerged as one of the Middle East’s fastest-growing economies, provides a sound case study for discussing some of the emerging dynamics of regional labor migration. This chapter examines Arab-origin migration to Qatar, reviewing how the state has negotiated the entry and control of “alien” Arabs. The chapter examines the evolution and transformation of migration patterns to the Gulf Cooperation Council, and assesses policies adopted by the states to better manage their regional labor markets and control the flow of foreigners. Particular attention is given to scrutinizing how and why Qatar has become more selective and politicized in negotiating labor migration, and how this has impacted the Arab expatriate population.
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Kemp, Darrell J. Habitat selection and territoriality. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198797500.003.0006.

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Insects dominate virtually all terrestrial and freshwater habitats on earth. This chapter reviews insect habitat selection, focusing on the occupation and defence of mating sites. First the adaptive basis of mating systems, sex roles, and behaviors in regard to habitat are established, then site occupation and defence in territorial species is explored. Resource-holding potential and resource value are discussed for how they determine aggressive motivation, as well as how contestants seek to gauge such parameters, with particular attention to the role of convention, drawing upon exemplar studies in damselflies and butterflies that have provided a narrative between theory and empiricism. Conventional and/or plastic behaviors are also discussed in terms of the presence and certainty of contestant roles, encompassing phenomena, such as residency confusion, nasty neighbours and interloper effects. The chapter concludes by discussing future avenues, foremost among which is the opportunity to synthesize empirical data across taxa.
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Alex, McKay, ed. Tibet and her neighbours: A history. London: Edition Hansjörg Mayer, 2003.

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Birch, Jonathan. Two Conceptions of Social Fitness. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780198733058.003.0005.

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Hamilton introduced two conceptions of social fitness, which he termed neighbour-modulated fitness and inclusive fitness, and he argued that the two concepts are equivalent. This argument relies on two assumptions—actor’s control and additivity—that can be justified as approximations under ‘δ‎-weak selection’, which is selection on tiny differences between the mutant and the wild type. The assumption of δ‎-weak selection stems from a methodological stance that takes cumulative adaptation to be the explanatory target of social evolution research, together with an empirical commitment to a gradualist picture of how cumulative adaptation arises. In a process of gradual, cumulative adaptation, short-term change can be calculated using either fitness concept, but inclusive fitness has a special role to play as the criterion for improvement and the standard for optimality.
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(Editor), Kai Olaf Lang, and Johannes Varwick (Editor), eds. European Neighbourhood Policy: Challenges for the Eu-policy Towards the New Neighbours. Barbara Budrich Publishers, 2007.

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Xu, Xi-Chong. The Australian Future Fund. Edited by Douglas Cumming, Geoffrey Wood, Igor Filatotchev, and Juliane Reinecke. Oxford University Press, 2017. http://dx.doi.org/10.1093/oxfordhb/9780198754800.013.15.

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This chapter outlines the creation and development of the Australian Future Fund (FF), showing how and why policy makers adopted this option to manage the challenges presented by the “good economic fortune” that occurred in Australia from the mid-1990s to 2010. It discusses the politics behind the governing structure of the FF, which is copied from its neighbor, New Zealand. Despite its initial emphasis on independence, political influence was inevitable when selecting and appointing the chairman of the Board of Guardians. The chapter examines the two key issues of “ethical investment” and “ethical operation” and shows the difficult balance of demands between diverse constituents; it identifies the space created by the expectation that the FF will behave in a similar fashion of all profit-maximization investors.
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Carina, Jahani, and Korn Agnes, eds. The Baloch and their neighbours: Ethnic and linguistic contact in Balochistan in historical and modern times. Wiesbaden: Reichert, 2003.

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Antons, Christoph. Intellectual Property in Asia. Edited by Rochelle Dreyfuss and Justine Pila. Oxford University Press, 2017. http://dx.doi.org/10.1093/oxfordhb/9780198758457.013.18.

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This chapter covers parts of Asia where there have been very significant recent developments in intellectual property (IP) law. IP reform in the region was initially driven by the concerns of industrialized countries about the lack of IP protection in Asian “miracle” economies. More recently, it has become an important topic in free trade and economic partnership agreement negotiations. The developments in the individual countries are discussed in the context of an “Asian development model,” which has often combined short and generalized laws with numerous implementing decrees and administrative discretion. This has allowed for the selective adaptation of IP models from elsewhere, with some countries now strongly promoting higher IP standards to their regional neighbors. However, different historical pathways to development and local circumstances suggest that it is difficult to develop regional role models for others or to explain differences about IP exclusively with the divide between “developed” and “developing” countries.
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Del Sarto, Raffaella A. Borderlands. Oxford University Press, 2021. http://dx.doi.org/10.1093/oso/9780198833550.001.0001.

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The book proposes a profound rethink of the complex relationship between Europe—defined here as the European Union and its members—and the states of the Mediterranean Middle East and North Africa (MENA), Europe’s ‘southern neighbours’. These relations are examined through a borderlands prism that conceives of this interaction as one between an empire of sorts that seeks to export its order beyond the border, and the empire’s southern borderlands. Focusing on trade relations on the one hand, and the cooperation on migration, borders, and security on the other, the book revisits the historical origins and modalities of Europe’s selective rule transfer to MENA states, the interests underwriting these policies, and the complex dynamics marking the interaction between the two sides over a twenty-year period (1995–2015). It shows that within a system of structurally asymmetric economic relations from which Europe and MENA elites benefit the most, single MENA governments have been co-opted into the management of border and migration control where they act as Europe’s gatekeepers. Combined with specific policy choices of MENA governments, Europe’s selective expansion of its rules, practices, and disaggregated borders have contributed to rising socio-economic inequalities and the strengthening of authoritarian rule in the ‘southern neighbourhood’, with Europe tacitly tolerating serious violations of the rights of refugees and migrants at its fringes. Challenging the self-proclaimed benevolent nature of European policies and the notion of ‘Fortress Europe’ alike, the findings of this study contribute to broader debates on power, dependence, and interdependence in the discipline of international relations.
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Book chapters on the topic "Neighbor selection"

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Dornaika, F., I. Kamal Aldine, and B. Cases. "Exemplar Selection Using Collaborative Neighbor Representation." In Lecture Notes in Computer Science, 439–50. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-19644-2_37.

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Laki, Sándor, and Tamás Lukovszki. "Balanced Neighbor Selection for BitTorrent-Like Networks." In Lecture Notes in Computer Science, 659–70. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-40450-4_56.

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Dornaika, Fadi, and I. Kamal Aldine. "Instance Selection Using Two Phase Collaborative Neighbor Representation." In Artificial Neural Networks and Machine Learning – ICANN 2014, 121–28. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-11179-7_16.

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Skalak, David B. "Instance Sampling for Boosted and Standalone Nearest Neighbor Classifiers." In Instance Selection and Construction for Data Mining, 283–300. Boston, MA: Springer US, 2001. http://dx.doi.org/10.1007/978-1-4757-3359-4_16.

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Alimardani, Fateme, Reza Boostani, and Ebrahim Ansari. "Feature Selection SDA Method in Ensemble Nearest Neighbor Classifier." In Communications in Computer and Information Science, 884–87. Berlin, Heidelberg: Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-89985-3_125.

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Dai, Bi-Ru, and Shu-Ming Hsu. "An Instance Selection Algorithm Based on Reverse Nearest Neighbor." In Advances in Knowledge Discovery and Data Mining, 1–12. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-20841-6_1.

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An, Jingxin. "Efficient Clustering Algorithm in Dynamic Nearest Neighbor Selection Model." In Application of Intelligent Systems in Multi-modal Information Analytics, 274–79. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-51556-0_39.

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Lee, Soojung. "An Experimental Study for Neighbor Selection in Collaborative Filtering." In Lecture Notes in Electrical Engineering, 967–72. Berlin, Heidelberg: Springer Berlin Heidelberg, 2015. http://dx.doi.org/10.1007/978-3-662-46578-3_115.

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Tan, Wenan, Xiaofan Qin, and Qing Wang. "A Hybrid Collaborative Filtering Recommendation Algorithm Using Double Neighbor Selection." In Human Centered Computing, 416–27. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-15127-0_42.

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Kim, Yoo-Sung, Ki-Chang Kim, and Soo Duk Kim. "Prefetching Tiled Internet Data Using a Neighbor Selection Markov Chain." In Innovative Internet Computing Systems, 103–15. Berlin, Heidelberg: Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/3-540-48206-7_9.

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Conference papers on the topic "Neighbor selection"

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Aoki, Yusuke, and Kazuyuki Shudo. "Proximity Neighbor Selection in Blockchain Networks." In 2019 IEEE International Conference on Blockchain (Blockchain). IEEE, 2019. http://dx.doi.org/10.1109/blockchain.2019.00016.

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Kaveh-Yazdy, Fatemeh, Mohammad-Reza Zare-Mirakabad, and Feng Xia. "A novel neighbor selection approach for KNN." In the 1st International Workshop. New York, New York, USA: ACM Press, 2012. http://dx.doi.org/10.1145/2346604.2346607.

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Duan, Hancong, Xianliang Lu, Hui Tang, Xu Zhou, and Zhijun Zhao. "Proximity Neighbor Selection in Structured P2P Network." In The Sixth IEEE International Conference on Computer and Information Technology (CIT'06). IEEE, 2006. http://dx.doi.org/10.1109/cit.2006.154.

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Lei, Yingchun, Litang Yang, Qi Jiang, and Chanle Wu. "Experimental Views on Neighbor Selection in BitTorrent." In 2007 IFIP International Conference on Network and Parallel Computing Workshops (NPC 2007). IEEE, 2007. http://dx.doi.org/10.1109/icnpcw.2007.4351587.

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Yu, Guanghua, Jin Tian, and Minqiang Li. "Nearest neighbor-based instance selection for classification." In 2016 12th International Conference on Natural Computation and 13th Fuzzy Systems and Knowledge Discovery (ICNC-FSKD). IEEE, 2016. http://dx.doi.org/10.1109/fskd.2016.7603154.

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Shashirekha, H. L., and Agaz Hussain Wani. "Gene selection by Mutual Nearest Neighbor approach." In 2015 International Conference on Emerging Research in Electronics, Computer Science and Technology (ICERECT). IEEE, 2015. http://dx.doi.org/10.1109/erect.2015.7499048.

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Steele, Kevin L., Parris K. Egbert, and Bryan S. Morse. "Histogram Matching for Camera Pose Neighbor Selection." In Third International Symposium on 3D Data Processing, Visualization, and Transmission (3DPVT'06). IEEE, 2006. http://dx.doi.org/10.1109/3dpvt.2006.76.

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Lei, Yingchun, Litang Yang, Qi Jiang, and Chanle Wu. "Experimental Views on Neighbor Selection in BitTorrent." In 2007 IFIP International Conference on Network and Parallel Computing Workshops (NPC 2007). IEEE, 2007. http://dx.doi.org/10.1109/npc.2007.122.

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Dong, Dafan, Zi Hu, Ying Wu, Kai Yang, and Gongyi Wu. "Improving Neighbor Selection Mechanism of P2P Streaming." In 2009 International Symposium on Computer Network and Multimedia Technology (CNMT 2009). IEEE, 2009. http://dx.doi.org/10.1109/cnmt.2009.5374817.

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Yao, Z., and D. Loguinov. "Link Lifetimes and Randomized Neighbor Selection in DHTs." In 27th IEEE International Conference on Computer Communications (INFOCOM 2008). IEEE, 2008. http://dx.doi.org/10.1109/infocom.2007.38.

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Reports on the topic "Neighbor selection"

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Melnyk, Iurii. JUSTIFICATION OF OCCUPATION IN GERMAN (1938) AND RUSSIAN (2014) MEDIA: SUBSTITUTION OF AGGRESSOR AND VICTIM. Ivan Franko National University of Lviv, March 2021. http://dx.doi.org/10.30970/vjo.2021.50.11101.

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The article is dedicated to the examination and comparison of the justification of occupation of a neighboring country in the German (1938) and Russian (2014) media. The objective of the study is to reveal the mechanics of the application of the classical manipulative method of substituting of aggressor and victim on the material of German and Russian propaganda in 1938 and in 2014 respectively. According to the results of the study, clear parallels between the two information strategies can be traced at the level of the condemnation of internal aggression against a national minority loyal to Berlin / Moscow and its political representative (the Sudeten Germans – the pro-Russian Ukrainians, as well as the security forces of the Yanukovych regime); the reflections on dangers that Czechoslovakia / Ukraine poses to itself and to its neighbors; condemnation of the violation of the cultural rights of the minority that the occupier intends to protect (German language and culture – Russian language and culture); the historical parallels designed to deepen the modern conflict, to show it as a long-standing and a natural one (“Hussites” – “Banderites”). In the manipulative strategy of both media, the main focus is not on factual fabrication, but on the bias selection of facts, due to which the reader should have an unambiguous understanding of who is the permanent aggressor in the conflict (Czechoslovakia, Czechs – Ukraine, Ukrainians), and who is the permanent victim (Germans – Russians, Russian speakers). The substitution of victim and aggressor in the media in both cases became one of the most important manipulative strategies designed to justify the German occupation of part of Czechoslovakia and the Russian occupation of part of Ukraine.
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