Relevant bibliographies by topics / Neighbor analysis

Contents

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

Academic literature on the topic 'Neighbor analysis'

Author: Grafiati
Published: 4 June 2025
Last updated: 23 June 2025

Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles

Select a source type:

Consult the lists of relevant articles, books, theses, conference reports, and other scholarly sources on the topic 'Neighbor analysis.'

Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.

You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.

Journal articles on the topic "Neighbor analysis"

1

Cao, Shuchao, Feiyang Sun, Mohcine Chraibi, and Rui Jiang. "Spatial analysis for crowds in multi-directional flows based on large-scale experiments." Journal of Statistical Mechanics: Theory and Experiment 2021, no. 11 (2021): 113407. http://dx.doi.org/10.1088/1742-5468/ac3660.

Full text
Abstract:
Abstract In this paper, spatial analysis for the nearest neighbors is performed in the unidirectional, bidirectional and crossing flows. Based on the intended direction given in the experiment, different types of neighbors such as U-ped (neighbor with the same intended direction), B-ped (neighbor with the opposite intended direction) and C-ped (neighbor with the intersecting intended direction) are defined. The preferable positions of these neighbors during movement are investigated under various conditions. The spatial relation is quantified by calculating the distance and angle between the reference pedestrian and neighbors. The results indicate that the distribution of neighbors is closely related to the neighbor’s order, crowd density, neighbor type and flow type. Furthermore, the reasons that result in these distributions for different neighbors are explored. Finally neighbor distributions for different flows are compared and the implications of this research are discussed. The spatial analysis sheds new light on the study of pedestrian dynamics in a different perspective, which can help to develop and validate crowd models in the future.
APA, Harvard, Vancouver, ISO, and other styles
2

Mahfouz, Mohamed A. "INCORPORATING DENSITY IN K-NEAREST NEIGHBORS REGRESSION." International Journal of Advanced Research in Computer Science 14, no. 03 (2023): 144–49. http://dx.doi.org/10.26483/ijarcs.v14i3.6989.

Full text
Abstract:
The application of the traditional k-nearest neighbours in regression analysis suffers from several difficulties when only a limited number of samples are available. In this paper, two decision models based on density are proposed. In order to reduce testing time, a k-nearest neighbours table (kNN-Table) is maintained to keep the neighbours of each object x along with their weighted Manhattan distance to x and a binary vector representing the increase or the decrease in each dimension compared to x’s values. In the first decision model, if the unseen sample having a distance to one of its neighbours x less than the farthest neighbour of x’s neighbour then its label is estimated using linear interpolation otherwise linear extrapolation is used. In the second decision model, for each neighbour x of the unseen sample, the distance of the unseen sample to x and the binary vector are computed. Also, the set S of nearest neighbours of x are identified from the kNN-Table. For each sample in S, a normalized distance to the unseen sample is computed using the information stored in the kNN-Table and it is used to compute the weight of each neighbor of the neighbors of the unseen object. In the two models, a weighted average of the computed label for each neighbour is assigned to the unseen object. The diversity between the two proposed decision models and the traditional kNN regressor motivates us to develop an ensemble of the two proposed models along with traditional kNN regressor. The ensemble is evaluated and the results showed that the ensemble achieves significant increase in the performance compared to its base regressors and several related algorithms.
APA, Harvard, Vancouver, ISO, and other styles
3

QIU, XIPENG, and LIDE WU. "NEAREST NEIGHBOR DISCRIMINANT ANALYSIS." International Journal of Pattern Recognition and Artificial Intelligence 20, no. 08 (2006): 1245–59. http://dx.doi.org/10.1142/s0218001406005186.

Full text
Abstract:
Linear Discriminant Analysis (LDA) is a popular feature extraction technique in statistical pattern recognition. However, it often suffers from the small sample size problem when dealing with high-dimensional data. Moreover, while LDA is guaranteed to find the best directions when each class has a Gaussian density with a common covariance matrix, it can fail if the class densities are more general. In this paper, a novel nonparametric linear feature extraction method, nearest neighbor discriminant analysis (NNDA), is proposed from the view of the nearest neighbor classification. NNDA finds the important discriminant directions without assuming the class densities belong to any particular parametric family. It does not depend on the nonsingularity of the within-class scatter matrix either. Then we give an approximate approach to optimize NNDA and an extension to k-NN. We apply NNDA to the simulated data and real world data, the results demonstrate that NNDA outperforms the existing variant LDA methods.
APA, Harvard, Vancouver, ISO, and other styles
4

Nitzan, Irit, and Barak Libai. "If You Go, I Will Follow … Social Effects on the Decision to Terminate a Service." GfK Marketing Intelligence Review 5, no. 2 (2013): 40–45. http://dx.doi.org/10.2478/gfkmir-2014-0016.

Full text
Abstract:
Abstract The adoption of new behaviors or new products is often influenced by those closest to us, but customer churn is influenced by social factors as well. An analysis of 1 million customers of a cellular company showed that customers were 79.7 % more likely to defect for each time one of their social neighbors defected. The more a customer communicated with a neighbor and the more characteristics they shared, such as age, gender or status, the more likely the customer was to follow the neighbor in canceling the service. The effect of a neighbor’s defection on a focal customer’s hazard of defection was strongest within the first month and decreased over time. The study shows that companies should take customers’ social networks into account when attempting to predict and manage customer churn. Network-related information can substantially improve analysis of new product adoption, and the same seems true for the field of customer defection.
APA, Harvard, Vancouver, ISO, and other styles
5

Huang, Wenjun, Xu Li, and Yanan Liang. "Impact of Age Violation Probability on Neighbor Election-Based Distributed Slot Access in Wireless Ad Hoc Networks." Electronics 12, no. 2 (2023): 351. http://dx.doi.org/10.3390/electronics12020351.

Full text
Abstract:
In this paper, we propose an analytical model of neighbor election-based distributed slot access by considering the relationship between the age of information (AoI) and the slot access process of nodes in wireless ad hoc networks. A node first maintains the information updates from its neighbors by relaying and receiving messages and determines message transmission slots by holding elections with its relevant competing neighbors. We first find out and analyze the interaction relationship between the transmission probability of nodes, the competing probability of neighbor nodes, and the violation probability that AoI exceeds the timeliness threshold of the neighbor election. Next, we obtain the approximated expression of the competing probability and the age violation probability based on the comprehensive analysis of the neighbor election process. Numerical and simulation results show that our approximation is tighter than the one in the literature and also provides insights into enhancing the design of network-aware distributed multiple access schemes.
APA, Harvard, Vancouver, ISO, and other styles
6

Zheng, Jianwei, Hong Qiu, Xinli Xu, Wanliang Wang, and Qiongfang Huang. "Fast Discriminative Stochastic Neighbor Embedding Analysis." Computational and Mathematical Methods in Medicine 2013 (2013): 1–14. http://dx.doi.org/10.1155/2013/106867.

Full text
Abstract:
Feature is important for many applications in biomedical signal analysis and living system analysis. A fast discriminative stochastic neighbor embedding analysis (FDSNE) method for feature extraction is proposed in this paper by improving the existing DSNE method. The proposed algorithm adopts an alternative probability distribution model constructed based on itsK-nearest neighbors from the interclass and intraclass samples. Furthermore, FDSNE is extended to nonlinear scenarios using the kernel trick and then kernel-based methods, that is, KFDSNE1 and KFDSNE2. FDSNE, KFDSNE1, and KFDSNE2 are evaluated in three aspects: visualization, recognition, and elapsed time. Experimental results on several datasets show that, compared with DSNE and MSNP, the proposed algorithm not only significantly enhances the computational efficiency but also obtains higher classification accuracy.
APA, Harvard, Vancouver, ISO, and other styles
7

Lebedeva, Elena. "Role of Digital Tools in Community Management and Urban Participation (Evidence of Belarus)." Studia Humanistyczne AGH 21, no. 4 (2022): 23–35. http://dx.doi.org/10.7494/human.2022.21.4.23.

Full text
Abstract:
This article is devoted to an analysis of the �hybrid neighborhood� phenomenon. Traditionally, a Soviet residential yard is presented in urban studies as the sphere of a neighbor�s active participation. The post-Soviet changes have significantly weakened the activities of neighbor communities; however, the spread of digital communication tools (social networks and messengers) has led to an increase in civic engagement in cities (new forms of neighboring communities are created, traditions of spending time together with neighbors revived, and individuals are actively involving in the struggle for their �place in the city�). The empirical materials that are analyzed reveal the features of neighbors interacting demonstrate the differences between �neighbor� and �civil� communication modes, define the role of online communities in local self-government, and practically implement the �right to the city.�
APA, Harvard, Vancouver, ISO, and other styles
8

Wong, K., and G. B. Golding. "A phylogenetic analysis of the pSymB replicon from the Sinorhizobium meliloti genome reveals a complex evolutionary history." Canadian Journal of Microbiology 49, no. 4 (2003): 269–80. http://dx.doi.org/10.1139/w03-037.

Full text
Abstract:
Microbial genomes are thought to be mosaic, making it difficult to decipher how these genomes have evolved. Whole-genome nearest-neighbor analysis was applied to the Sinorhizobium meliloti pSymB replicon to determine its origin, the degree of horizontal transfer, and the conservation of gene order. Prediction of the nearest neighbor based on contextual information, i.e., the nearest phylogenetic neighbor of adjacent genes, provided useful information for genes for which phylogenetic relationships could not be established. A large portion of pSymB genes are most closely related to genes in the Agrobacterium tumefaciens linear chromosome, including the rep and min genes. This suggests a common origin for these replicons. Genes with the nearest neighbor from the same species tend to be grouped in "patches". Gene order within these patches is conserved, but the content of the patches is not limited to operons. These data show that 13% of pSymB genes have nearest neighbors in species that are not members of the Rhizobiaceae family (including two archaea), and that these likely represent genes that have been involved in horizontal transfer. Key words: Sinorhizobium meliloti, horizontal transfer, pSymB evolution.
APA, Harvard, Vancouver, ISO, and other styles
9

Song, Yunsheng, Xiaohan Kong, and Chao Zhang. "A Large-Scale k -Nearest Neighbor Classification Algorithm Based on Neighbor Relationship Preservation." Wireless Communications and Mobile Computing 2022 (January 7, 2022): 1–11. http://dx.doi.org/10.1155/2022/7409171.

Full text
Abstract:
Owing to the absence of hypotheses of the underlying distributions of the data and the strong generation ability, the k -nearest neighbor (kNN) classification algorithm is widely used to face recognition, text classification, emotional analysis, and other fields. However, kNN needs to compute the similarity between the unlabeled instance and all the training instances during the prediction process; it is difficult to deal with large-scale data. To overcome this difficulty, an increasing number of acceleration algorithms based on data partition are proposed. However, they lack theoretical analysis about the effect of data partition on classification performance. This paper has made a theoretical analysis of the effect using empirical risk minimization and proposed a large-scale k -nearest neighbor classification algorithm based on neighbor relationship preservation. The process of searching the nearest neighbors is converted to a constrained optimization problem. Then, it gives the estimation of the difference on the objective function value under the optimal solution with data partition and without data partition. According to the obtained estimation, minimizing the similarity of the instances in the different divided subsets can largely reduce the effect of data partition. The minibatch k -means clustering algorithm is chosen to perform data partition for its effectiveness and efficiency. Finally, the nearest neighbors of the test instance are continuously searched from the set generated by successively merging the candidate subsets until they do not change anymore, where the candidate subsets are selected based on the similarity between the test instance and cluster centers. Experiment results on public datasets show that the proposed algorithm can largely keep the same nearest neighbors and no significant difference in classification accuracy as the original kNN classification algorithm and better results than two state-of-the-art algorithms.
APA, Harvard, Vancouver, ISO, and other styles
10

Kim, Sungeun, and Seran Jeon. "Latent Class Analysis of Discrimination and Social Capital in Korean Public Rental Housing Communities." Buildings 15, no. 3 (2025): 337. https://doi.org/10.3390/buildings15030337.

Full text
Abstract:
This study explored typologies among residents of South Korean public rental housing, focusing on their experiences of discrimination and social capital. Latent class analysis (LCA) was applied to data from 4683 individuals in the 2021 Seoul Public Rental Housing Panel Survey. Four distinct groups were identified: ‘Group Seeking Friendly Neighbor Relationships’, ‘Group Accepting Losses’, ‘Group with High Social Capital’, and ‘Group Indifferent to Neighbors’. The findings revealed that while discrimination was widespread, certain groups exhibited strong social capital. Notably, the ‘Group Accepting Losses’ showed the highest willingness to help neighbors despite facing significant discrimination, while the ‘Group with High Social Capital’ displayed high levels of neighbor trust and mutual support. These results challenge traditional views by showing that social capital can thrive even in the presence of discrimination. This study suggests that policies aimed at addressing discrimination in public rental housing should focus not only on physical integration but also on fostering social connections to enhance community cohesion and reduce mental health issues among residents.
APA, Harvard, Vancouver, ISO, and other styles
More sources

Dissertations / Theses on the topic "Neighbor analysis"

1

Shen, Qiong Mao. "Group nearest neighbor queries /." View abstract or full-text, 2003. http://library.ust.hk/cgi/db/thesis.pl?COMP%202003%20SHEN.

Full text
APA, Harvard, Vancouver, ISO, and other styles
2

Hui, Michael Chun Kit. "Aggregate nearest neighbor queries /." View abstract or full-text, 2004. http://library.ust.hk/cgi/db/thesis.pl?COMP%202004%20HUI.

Full text
Abstract:
Thesis (M. Phil.)--Hong Kong University of Science and Technology, 2004.<br>Includes bibliographical references (leaves 91-95). Also available in electronic version. Access restricted to campus users.
APA, Harvard, Vancouver, ISO, and other styles
3

Lee, Jong-Seok. "Preserving nearest neighbor consistency in cluster analysis." [Ames, Iowa : Iowa State University], 2009. http://gateway.proquest.com/openurl?url_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:dissertation&res_dat=xri:pqdiss&rft_dat=xri:pqdiss:3369852.

Full text
APA, Harvard, Vancouver, ISO, and other styles
4

Li, Zhe. "Performance Analysis of Network Assisted Neighbor Discovery Algorithms." Thesis, KTH, Reglerteknik, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-117696.

Full text
Abstract:
Recently there has been an increasing interest in applications that enable users in the proximity of one another to share experiences, discover surrounding events, play online games and in general develop proximity based social networks. Most of the existing applications are based on cellular network communications, combined with over-the-top (OTT) solutions involving either registration at an application server and/or obtaining location information from a positioning system such as Global Positioning System (GPS). However, registration at a server often requires continuous registration updates due to, for example, mobility and changes in user population, which is a tedious and resource consuming process. In addition, using GPS drains the battery of devices. Since the spectrum used for cellular network is limited, it can become a scarce resource with increasing quantity of the devices. In order to deal with these problems, the concept of direct Device-to-Device (D2D) communication has been proposed as a solution. Using D2D technology, devices can discover nearby devices without extra positioning information. It can not only increase the spectrum efficiency, but also improve the coverage of cellular network. The discovery of devices can be prepared before the actual communication phase or proceed simultaneously. In this work, we mainly investigate the former one, which is called a-priori discovery. In fact, a-priory device discovery provides a value on its own right, independently of a subsequent communication phase using D2D or traditional cellular communication. Previous studies indicate that ad hoc D2D discovery (i.e. without cellular network assistance) is feasible but time, resource and energy consuming. Recognizing this problem, both academia and industry pay more attention to the D2D discovery in cellular spectrum, where D2D discovery can be assisted by a cellular radio access network. Despite this interest, to the best of our knowledge, there is essentially no work on identifying different degrees of network assistance (that we call the “network assistance levels”) and evaluating the potential gains of specific netw ork assistance algorithms. Therefore, in this thesis work we develop algorithms that take advantage of network assistance to improve the performance of the ad hoc neighbor discovery algorithms in terms of energy efficiency, resource utilization, discovery time and discovery rate. To address the equirements of different applications and types of devices, two design objectives are studied in this work. The first one is discovery time prioritized without energy limitation, while the other is constrained to using a certain amount of energy. We distinguish five levels of network involvement from allowing for synchronization to explicitly providing information on the used peer discovery resources. The analysis in this work indicates that the setting of transmission probability for devices, which depends on system load, plays a critical role in the process of D2D discovery. Furthermore, stopping the devices which have already been discovered by enough candidates can improve the performance, in terms of reducing the interference to other devices and saving energy consumption. It is also shown in the simulation results that, to reach a given quantity of D2D communication candidates for all the devices in the area of study, the discovery time as well as the energy consumption can be reduced up to 87-91% from the lowest level of the network assistance to the highest level.
APA, Harvard, Vancouver, ISO, and other styles
5

Ram, Parikshit. "New paradigms for approximate nearest-neighbor search." Diss., Georgia Institute of Technology, 2013. http://hdl.handle.net/1853/49112.

Full text
Abstract:
Nearest-neighbor search is a very natural and universal problem in computer science. Often times, the problem size necessitates approximation. In this thesis, I present new paradigms for nearest-neighbor search (along with new algorithms and theory in these paradigms) that make nearest-neighbor search more usable and accurate. First, I consider a new notion of search error, the rank error, for an approximate neighbor candidate. Rank error corresponds to the number of possible candidates which are better than the approximate neighbor candidate. I motivate this notion of error and present new efficient algorithms that return approximate neighbors with rank error no more than a user specified amount. Then I focus on approximate search in a scenario where the user does not specify the tolerable search error (error constraint); instead the user specifies the amount of time available for search (time constraint). After differentiating between these two scenarios, I present some simple algorithms for time constrained search with provable performance guarantees. I use this theory to motivate a new space-partitioning data structure, the max-margin tree, for improved search performance in the time constrained setting. Finally, I consider the scenario where we do not require our objects to have an explicit fixed-length representation (vector data). This allows us to search with a large class of objects which include images, documents, graphs, strings, time series and natural language. For nearest-neighbor search in this general setting, I present a provably fast novel exact search algorithm. I also discuss the empirical performance of all the presented algorithms on real data.
APA, Harvard, Vancouver, ISO, and other styles
6

Xie, Xike, and 谢希科. "Evaluating nearest neighbor queries over uncertain databases." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2012. http://hub.hku.hk/bib/B4784954X.

Full text
Abstract:
Nearest Neighbor (NN in short) queries are important in emerging applications, such as wireless networks, location-based services, and data stream applications, where the data obtained are often imprecise. The imprecision or imperfection of the data sources is modeled by uncertain data in recent research works. Handling uncertainty is important because this issue affects the quality of query answers. Although queries on uncertain data are useful, evaluating the queries on them can be costly, in terms of I/O or computational efficiency. In this thesis, we study how to efficiently evaluate NN queries on uncertain data. Given a query point q and a set of uncertain objects O, the possible nearest neighbor query returns a set of candidates which have non-zero probabilities to be the query answer. It is also interesting to ask \which region has the same set of possible nearest neighbors", and \which region has one specific object as its possible nearest neighbor". To reveal the relationship between the query space and nearest neighbor answers, we propose the UV-diagram, where the query space is split into disjoint partitions, such that each partition is associated with a set of objects. If a query point is located inside the partition, its possible nearest neighbors could be directly retrieved. However, the number of such partitions is exponential and the construction effort can be expensive. To tackle this problem, we propose an alternative concept, called UV-cell, and efficient algorithms for constructing it. The UV-cell has an irregular shape, which incurs difficulties in storage, maintenance, and query evaluation. We design an index structure, called UV-index, which is an approximated version of the UV-diagram. Extensive experiments show that the UV-index could efficiently answer different variants of NN queries, such as Probabilistic Nearest Neighbor Queries, Continuous Probabilistic Nearest Neighbor Queries. Another problem studied in this thesis is the trajectory nearest neighbor query. Here the query point is restricted to a pre-known trajectory. In applications (e.g. monitoring potential threats along a flight/vessel's trajectory), it is useful to derive nearest neighbors for all points on the query trajectory. Simple solutions, such as sampling or approximating the locations of uncertain objects as points, fails to achieve a good query quality. To handle this problem, we design efficient algorithms and optimization methods for this query. Experiments show that our solution can efficiently and accurately answer this query. Our solution is also scalable to large datasets and long trajectories.<br>published_or_final_version<br>Computer Science<br>Doctoral<br>Doctor of Philosophy
APA, Harvard, Vancouver, ISO, and other styles
7

Zhang, Jun. "Nearest neighbor queries in spatial and spatio-temporal databases /." View abstract or full-text, 2003. http://library.ust.hk/cgi/db/thesis.pl?COMP%202003%20ZHANG.

Full text
APA, Harvard, Vancouver, ISO, and other styles
8

Zhang, Peiwu, and 张培武. "Voronoi-based nearest neighbor search for multi-dimensional uncertain databases." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2012. http://hub.hku.hk/bib/B49618179.

Full text
Abstract:
In Voronoi-based nearest neighbor search, the Voronoi cell of every point p in a database can be used to check whether p is the closest to some query point q. We extend the notion of Voronoi cells to support uncertain objects, whose attribute values are inexact. Particularly, we propose the Possible Voronoi cell (or PV-cell). A PV-cell of a multi-dimensional uncertain object o is a region R, such that for any point p ∈ R, o may be the nearest neighbor of p. If the PV-cells of all objects in a database S are known, they can be used to identify objects that have a chance to be the nearest neighbor of q. However, there is no efficient algorithm for computing an exact PV-cell. We hence study how to derive an axis-parallel hyper-rectangle (called the Uncertain Bounding Rectangle, or UBR) that tightly contains a PV-cell. We further develop the PV-index, a structure that stores UBRs, to evaluate probabilistic nearest neighbor queries over uncertain data. An advantage of the PV-index is that upon updates on S, it can be incrementally updated. Extensive experiments on both synthetic and real datasets are carried out to validate the performance of the PV-index.<br>published_or_final_version<br>Computer Science<br>Master<br>Master of Philosophy
APA, Harvard, Vancouver, ISO, and other styles
9

Wong, Wing Sing. "K-nearest-neighbor queries with non-spatial predicates on range attributes /." View abstract or full-text, 2005. http://library.ust.hk/cgi/db/thesis.pl?COMP%202005%20WONGW.

Full text
APA, Harvard, Vancouver, ISO, and other styles
10

Yiu, Man-lung. "Advanced query processing on spatial networks." Click to view the E-thesis via HKUTO, 2006. http://sunzi.lib.hku.hk/hkuto/record/B36279365.

Full text
APA, Harvard, Vancouver, ISO, and other styles
More sources

Books on the topic "Neighbor analysis"

1

V, Dasarathy Belur, ed. Nearest neighbor (NN) norms: Nn pattern classification techniques. IEEE Computer Society Press, 1991.

Find full text
APA, Harvard, Vancouver, ISO, and other styles
2

Weber, Roger. Similarity search in high dimensional vector spaces. Aka, 2001.

Find full text
APA, Harvard, Vancouver, ISO, and other styles
3

Kramer, Oliver. Dimensionality Reduction with Unsupervised Nearest Neighbors. Springer Berlin Heidelberg, 2013.

Find full text
APA, Harvard, Vancouver, ISO, and other styles
4

Ndubisi, Bennet E. C. Ihiala and her neighbours: A historical and socio-cultural analysis. D'man Litho Services, 1996.

Find full text
APA, Harvard, Vancouver, ISO, and other styles
5

Ndubisi, Bennett E. C. Ihiala and her neighbours: A historical and socio-cultural analysis. D'man Litho Services, 1996.

Find full text
APA, Harvard, Vancouver, ISO, and other styles
6

Brunnermeier, Markus K. Contrasting different forms of price stickiness: An analysis of exchange rate overshooting and the beggar thy neighbour policy. London School of Economics, Financial Markets Group, 1999.

Find full text
APA, Harvard, Vancouver, ISO, and other styles
7

Ndem, Eyo B. E. Calabar from 1500-2000. Efik cultural imperialism. An Analysis of Efik symbiotic relationship with her neighbours. S.l., 1986.

Find full text
APA, Harvard, Vancouver, ISO, and other styles
8

Manolopoulos, Yannis, and Apostolos N. N. Papadopoulos. Nearest Neighbor Search : : A Database Perspective. Springer, 2010.

Find full text
APA, Harvard, Vancouver, ISO, and other styles
9

Bhatti, Aman Ullah. Application of geostatistics and nearest neighbor analysis methods to statistical analysis of field experiments. 1990.

Find full text
APA, Harvard, Vancouver, ISO, and other styles
10

Papadopoulos, Apostolos N., and Yannis Manolopoulos. Nearest Neighbor Search : : A Database Perspective. Springer London, Limited, 2006.

Find full text
APA, Harvard, Vancouver, ISO, and other styles
More sources

Book chapters on the topic "Neighbor analysis"

1

Smith, Wayne W. "Nearest neighbor analysis." In Encyclopedia of Tourism. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-01384-8_380.

Full text
APA, Harvard, Vancouver, ISO, and other styles
2

Smith, Wayne W. "Nearest Neighbor Analysis." In Encyclopedia of Tourism. Springer Nature Switzerland, 2024. https://doi.org/10.1007/978-3-030-74923-1_380.

Full text
APA, Harvard, Vancouver, ISO, and other styles
3

Smith, Wayne W. "Nearest neighbor analysis, tourism." In Encyclopedia of Tourism. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-01669-6_380-1.

Full text
APA, Harvard, Vancouver, ISO, and other styles
4

Smith, Wayne W. "Nearest Neighbor Analysis in Tourism." In Encyclopedia of Tourism. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-319-01669-6_380-2.

Full text
APA, Harvard, Vancouver, ISO, and other styles
5

Zhu, Wei, and M. Victor Wickerhauser. "Wavelet Transforms by Nearest Neighbor Lifting." In Excursions in Harmonic Analysis, Volume 2. Birkhäuser Boston, 2012. http://dx.doi.org/10.1007/978-0-8176-8379-5_9.

Full text
APA, Harvard, Vancouver, ISO, and other styles
6

Saupe, Dietmar. "Fractal Image Compression via Nearest Neighbor Search." In Fractal Image Encoding and Analysis. Springer Berlin Heidelberg, 1998. http://dx.doi.org/10.1007/978-3-662-03512-2_6.

Full text
APA, Harvard, Vancouver, ISO, and other styles
7

Vlachos, Dimitrios, and Dimitrios Thomakos. "Nearest Neighbor Forecasting Using Sparse Data Representation." In Mathematical Analysis in Interdisciplinary Research. Springer International Publishing, 2012. http://dx.doi.org/10.1007/978-3-030-84721-0_38.

Full text
APA, Harvard, Vancouver, ISO, and other styles
8

Globig, Christoph, and Stefan Wess. "Symbolic Learning and Nearest-Neighbor Classification." In Studies in Classification, Data Analysis, and Knowledge Organization. Springer Berlin Heidelberg, 1994. http://dx.doi.org/10.1007/978-3-642-46808-7_2.

Full text
APA, Harvard, Vancouver, ISO, and other styles
9

Borgohain, Olimpia, Meghna Dasgupta, Piyush Kumar, and Gitimoni Talukdar. "Performance Analysis of Nearest Neighbor, K-Nearest Neighbor and Weighted K-Nearest Neighbor for the Classification of Alzheimer Disease." In Advances in Intelligent Systems and Computing. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-7394-1_28.

Full text
APA, Harvard, Vancouver, ISO, and other styles
10

Agarwal, Yash, and G. Poornalatha. "Analysis of the Nearest Neighbor Classifiers: A Review." In Advances in Intelligent Systems and Computing. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-3514-7_43.

Full text
APA, Harvard, Vancouver, ISO, and other styles

Conference papers on the topic "Neighbor analysis"

1

NONG, JIAN, Jian Xu, Xi He, Wenge Li, and Hongben Huang. "Octree k-nearest neighbor parallel query." In Fourth International Conference on Computer Vision and Pattern Analysis (ICCPA 2024), edited by Ji Zhao and Yonghui Yang. SPIE, 2024. http://dx.doi.org/10.1117/12.3037976.

Full text
APA, Harvard, Vancouver, ISO, and other styles
2

Iwabuchi, Keita, Trevor Steil, Benjamin W. Priest, Roger Pearce, and Geoffrey Sanders. "NEO-DNND: Communication-Optimized Distributed Nearest Neighbor Graph Construction." In SC24-W: Workshops of the International Conference for High Performance Computing, Networking, Storage and Analysis. IEEE, 2024. https://doi.org/10.1109/scw63240.2024.00096.

Full text
APA, Harvard, Vancouver, ISO, and other styles
3

Ren, Jinlin, and Yang Gong. "Rating Analysis and Prediction Based on K-Nearest Neighbor Regression." In 2024 4th International Conference on Electronic Information Engineering and Computer Communication (EIECC). IEEE, 2024. https://doi.org/10.1109/eiecc64539.2024.10929208.

Full text
APA, Harvard, Vancouver, ISO, and other styles
4

Armando Sibuea, Alvin Tolopan, Putu Harry Gunawan, and Indwiarti. "Classifying Stunting Status in Toddlers Using K-Nearest Neighbor and Logistic Regression Analysis." In 2024 International Conference on Data Science and Its Applications (ICoDSA). IEEE, 2024. http://dx.doi.org/10.1109/icodsa62899.2024.10652063.

Full text
APA, Harvard, Vancouver, ISO, and other styles
5

Khastavaneh, Hassan, Hossein Ebrahimpour-Komleh, and Amin Hanaee-Ahwaz. "Unknown aware k nearest neighbor classifier." In 2017 3rd International Conference on Pattern Recognition and Image Analysis (IPRIA). IEEE, 2017. http://dx.doi.org/10.1109/pria.2017.7983027.

Full text
APA, Harvard, Vancouver, ISO, and other styles
6

Chang, Cheng, and Run-Tao Liu. "The furthest neighbor query of space K." In 2015 International Conference on Wavelet Analysis and Pattern Recognition (ICWAPR). IEEE, 2015. http://dx.doi.org/10.1109/icwapr.2015.7295927.

Full text
APA, Harvard, Vancouver, ISO, and other styles
7

Li, Jing, and Ming Cheng. "Analysis of the k-nearest neighbor classification." In 2013 International Conference of Information Science and Management Engineering. WIT Press, 2013. http://dx.doi.org/10.2495/isme132482.

Full text
APA, Harvard, Vancouver, ISO, and other styles
8

Sampedro, Gabriel Avelino R., Maricor N. Soriano, Analyn N. Yumang, and Ericson D. Dimaunahan. "Rapid Microscopic Analysis Using Natural Neighbor Interpolation." In the 2019 9th International Conference. ACM Press, 2019. http://dx.doi.org/10.1145/3326172.3326218.

Full text
APA, Harvard, Vancouver, ISO, and other styles
9

Xiaorong, Feng, Lin Jun, and Jia Shizhun. "Security analysis for IPv6 neighbor discovery protocol." In 2013 2nd International Symposium on Instrumentation & Measurement, Sensor Network and Automation (IMSNA). IEEE, 2013. http://dx.doi.org/10.1109/imsna.2013.6743275.

Full text
APA, Harvard, Vancouver, ISO, and other styles
10

Sadjadi, Seyed Omid, Jason W. Pelecanos, and Sriram Ganapathy. "Nearest neighbor discriminant analysis for language recognition." In ICASSP 2015 - 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2015. http://dx.doi.org/10.1109/icassp.2015.7178763.

Full text
APA, Harvard, Vancouver, ISO, and other styles

Reports on the topic "Neighbor analysis"

1

Weingart, M., and S. Selvin. Nearest neighbor analysis in one dimension. Office of Scientific and Technical Information (OSTI), 1995. http://dx.doi.org/10.2172/33152.

Full text
APA, Harvard, Vancouver, ISO, and other styles
2

Kukec, A., S. Krishnan, and S. Jiang. The Secure Neighbor Discovery (SEND) Hash Threat Analysis. RFC Editor, 2011. http://dx.doi.org/10.17487/rfc6273.

Full text
APA, Harvard, Vancouver, ISO, and other styles
3

Kiessling, L. L., and P. B. Dervan. Analysis of Nearest Neighbor Interactions in the Pyrimidine Triple Helix Motif by Affinity Cleaving. Defense Technical Information Center, 1991. http://dx.doi.org/10.21236/ada237527.

Full text
APA, Harvard, Vancouver, ISO, and other styles
4

Hoen, Ben, Ryan Wiser, and Haftan Eckholdt. Assessing the Impacts of Reduced Noise Operations of Wind Turbines on Neighbor Annoyance: A Preliminary Analysis in Vinalhaven, Maine. Office of Scientific and Technical Information (OSTI), 2010. http://dx.doi.org/10.2172/984315.

Full text
APA, Harvard, Vancouver, ISO, and other styles
5

Cerulli, Giovanni. Machine Learning and AI for Research in Python. Instats Inc., 2023. http://dx.doi.org/10.61700/b7qz5fpva9dar469.

Full text
Abstract:
This seminar is an introduction to Machine Learning and Artificial Intelligence methods for the social, economic, and health sciences using Python. After introducing the subject, the seminar will cover the following methods: (i) model selection and regularization (Lasso, Ridge, Elastic-net); (ii) discriminant analysis and nearest-neighbor classification; and (iii) artificial neural networks. The course will offer various instructional examples using real datasets in Python. An Instats certificate of completion is provided at the end of the seminar, and 2 ECTS equivalent points are offered.
APA, Harvard, Vancouver, ISO, and other styles
6

Cerulli, Giovanni. Machine Learning and AI for Research in Python. Instats Inc., 2023. http://dx.doi.org/10.61700/x92cmhdrxsvu7469.

Full text
Abstract:
This seminar is an introduction to Machine Learning and Artificial Intelligence methods for the social, economic, and health sciences using Python. After introducing the subject, the seminar will cover the following methods: (i) model selection and regularization (Lasso, Ridge, Elastic-net); (ii) discriminant analysis and nearest-neighbor classification; and (iii) artificial neural networks. The course will offer various instructional examples using real datasets in Python. An Instats certificate of completion is provided at the end of the seminar, and 2 ECTS equivalent points are offered.
APA, Harvard, Vancouver, ISO, and other styles
7

Cerulli, Giovanni. Machine Learning and AI for Researchers in R. Instats Inc., 2023. http://dx.doi.org/10.61700/n8rzaz6kghskt469.

Full text
Abstract:
This seminar is an introduction to Machine Learning and Artificial Intelligence methods for the social, economic, and health sciences using R. After introducing the subject, the seminar will cover the following methods: (i) model selection and regularization (Lasso, Ridge, Elastic-net, and subset-selection models); (ii) discriminant analysis and nearest-neighbor classification; and (iii) artificial neural networks. The course will offer various instructional examples using real datasets in R. An Instats certificate of completion is provided at the end of the seminar, and 2 ECTS equivalent points are offered.
APA, Harvard, Vancouver, ISO, and other styles
8

Cerulli, Giovanni. Machine Learning and AI for Researchers in R. Instats Inc., 2023. http://dx.doi.org/10.61700/atz7nxsz9afbm469.

Full text
Abstract:
This seminar is an introduction to Machine Learning and Artificial Intelligence methods for the social, economic, and health sciences using R. After introducing the subject, the seminar will cover the following methods: (i) model selection and regularization (Lasso, Ridge, Elastic-net, and subset-selection models); (ii) discriminant analysis and nearest-neighbor classification; and (iii) artificial neural networks. The course will offer various instructional examples using real datasets in R. An Instats certificate of completion is provided at the end of the seminar, and 2 ECTS equivalent points are offered.
APA, Harvard, Vancouver, ISO, and other styles
9

Cerulli, Giovanni. Machine Learning and AI for Researchers in R. Instats Inc., 2023. http://dx.doi.org/10.61700/w5nn12uvjosgd469.

Full text
Abstract:
This seminar is an introduction to Machine Learning and Artificial Intelligence methods for the social, economic, and health sciences using R. After introducing the subject, the seminar will cover the following methods: (i) model selection and regularization (Lasso, Ridge, Elastic-net, and subset-selection models); (ii) discriminant analysis and nearest-neighbor classification; and (iii) artificial neural networks. The course will offer various instructional examples using real datasets in R. An Instats certificate of completion is provided at the end of the seminar, and 2 ECTS equivalent points are offered.
APA, Harvard, Vancouver, ISO, and other styles
10

Searcy, Stephen W., and Kalman Peleg. Adaptive Sorting of Fresh Produce. United States Department of Agriculture, 1993. http://dx.doi.org/10.32747/1993.7568747.bard.

Full text
Abstract:
This project includes two main parts: Development of a “Selective Wavelength Imaging Sensor” and an “Adaptive Classifiery System” for adaptive imaging and sorting of agricultural products respectively. Three different technologies were investigated for building a selectable wavelength imaging sensor: diffraction gratings, tunable filters and linear variable filters. Each technology was analyzed and evaluated as the basis for implementing the adaptive sensor. Acousto optic tunable filters were found to be most suitable for the selective wavelength imaging sensor. Consequently, a selectable wavelength imaging sensor was constructed and tested using the selected technology. The sensor was tested and algorithms for multispectral image acquisition were developed. A high speed inspection system for fresh-market carrots was built and tested. It was shown that a combination of efficient parallel processing of a DSP and a PC based host CPU in conjunction with a hierarchical classification system, yielded an inspection system capable of handling 2 carrots per second with a classification accuracy of more than 90%. The adaptive sorting technique was extensively investigated and conclusively demonstrated to reduce misclassification rates in comparison to conventional non-adaptive sorting. The adaptive classifier algorithm was modeled and reduced to a series of modules that can be added to any existing produce sorting machine. A simulation of the entire process was created in Matlab using a graphical user interface technique to promote the accessibility of the difficult theoretical subjects. Typical Grade classifiers based on k-Nearest Neighbor techniques and linear discriminants were implemented. The sample histogram, estimating the cumulative distribution function (CDF), was chosen as a characterizing feature of prototype populations, whereby the Kolmogorov-Smirnov statistic was employed as a population classifier. Simulations were run on artificial data with two-dimensions, four populations and three classes. A quantitative analysis of the adaptive classifier's dependence on population separation, training set size, and stack length determined optimal values for the different parameters involved. The technique was also applied to a real produce sorting problem, e.g. an automatic machine for sorting dates by machine vision in an Israeli date packinghouse. Extensive simulations were run on actual sorting data of dates collected over a 4 month period. In all cases, the results showed a clear reduction in classification error by using the adaptive technique versus non-adaptive sorting.
APA, Harvard, Vancouver, ISO, and other styles
You might also be interested in the extended bibliographies on the topic 'Neighbor analysis' for particular source types:
Journal articles Dissertations / Theses Books
We offer discounts on all premium plans for authors whose works are included in thematic literature selections. Contact us to get a unique promo code!

To the bibliography