Multiple-instance learning with pairwise instance similarity
Liming Yuan, Jiafeng Liu, Xianglong Tang (2014)
International Journal of Applied Mathematics and Computer Science
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Liming Yuan, Jiafeng Liu, Xianglong Tang (2014)
International Journal of Applied Mathematics and Computer Science
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Zengyou He, Xiaofei Xu, Joshua Zhexue Huang, Shengchun Deng (2005)
Computer Science and Information Systems
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Urszula Boryczka (2010)
Control and Cybernetics
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Zengyou He, Xiaofei Xu, Shenchun Deng (2006)
Computer Science and Information Systems
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Ohn San, Van-Nam Huynh, Yoshiteru Nakamori (2004)
International Journal of Applied Mathematics and Computer Science
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Most of the earlier work on clustering has mainly been focused on numerical data whose inherent geometric properties can be exploited to naturally define distance functions between data points. Recently, the problem of clustering categorical data has started drawing interest. However, the computational cost makes most of the previous algorithms unacceptable for clustering very large databases. The -means algorithm is well known for its efficiency in this respect. At the same time, working...
Sam Cole, Shmuel Friedland, Lev Reyzin (2017)
Special Matrices
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In this paper, we consider the planted partition model, in which n = ks vertices of a random graph are partitioned into k “clusters,” each of size s. Edges between vertices in the same cluster and different clusters are included with constant probability p and q, respectively (where 0 ≤ q < p ≤ 1). We give an efficient algorithm that, with high probability, recovers the clusters as long as the cluster sizes are are least (√n). Informally, our algorithm constructs the projection operator...
Ireneusz Czarnowski, Piotr Jędrzejowicz (2011)
International Journal of Applied Mathematics and Computer Science
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The problem considered concerns data reduction for machine learning. Data reduction aims at deciding which features and instances from the training set should be retained for further use during the learning process. Data reduction results in increased capabilities and generalization properties of the learning model and a shorter time of the learning process. It can also help in scaling up to large data sources. The paper proposes an agent-based data reduction approach with the learning...
Ch. Aswani Kumar (2009)
Computer Science and Information Systems
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Rafał Zdunek (2014)
International Journal of Applied Mathematics and Computer Science
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Rafał Zdunek (2014)
International Journal of Applied Mathematics and Computer Science
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Victor L. Brailovsky, Michael Har-Even (1998)
Kybernetika
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Detecting a cluster structure is considered. This means solving either the problem of discovering a natural decomposition of data points into groups (clusters) or the problem of detecting clouds of data points of a specific form. In this paper both these problems are considered. To discover a cluster structure of a specific arrangement or a cloud of data of a specific form a class of nonlinear projections is introduced. Fitness functions that estimate to what extent a given subset of...
Bill, Jo, Fokoue, Ernest (2014)
Serdica Journal of Computing
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This research evaluates pattern recognition techniques on a subclass of big data where the dimensionality of the input space (p) is much larger than the number of observations (n). Specifically, we evaluate massive gene expression microarray cancer data where the ratio κ is less than one. We explore the statistical and computational challenges inherent in these high dimensional low sample size (HDLSS) problems and present statistical machine learning methods used to tackle and circumvent...