4,825 research outputs found
Efficient Parallel Processing of k-Nearest Neighbor Queries by Using a Centroid-based and Hierarchical Clustering Algorithm
The k-Nearest Neighbor method is one of the most popular techniques for both classification and regression purposes. Because of its operation, the application of this classification may be limited to problems with a certain number of instances, particularly, when run time is a consideration. However, the classification of large amounts of data has become a fundamental task in many real-world applications. It is logical to scale the k-Nearest Neighbor method to large scale datasets. This paper proposes a new k-Nearest Neighbor classification method (KNN-CCL) which uses a parallel centroid-based and hierarchical clustering algorithm to separate the sample of training dataset into multiple parts. The introduced clustering algorithm uses four stages of successive refinements and generates high quality clusters. The k-Nearest Neighbor approach subsequently makes use of them to predict the test datasets. Finally, sets of experiments are conducted on the UCI datasets. The experimental results confirm that the proposed k-Nearest Neighbor classification method performs well with regard to classification accuracy and performance
A Generalized Framework for Agglomerative Clustering of Signed Graphs applied to Instance Segmentation
We propose a novel theoretical framework that generalizes algorithms for
hierarchical agglomerative clustering to weighted graphs with both attractive
and repulsive interactions between the nodes. This framework defines GASP, a
Generalized Algorithm for Signed graph Partitioning, and allows us to explore
many combinations of different linkage criteria and cannot-link constraints. We
prove the equivalence of existing clustering methods to some of those
combinations, and introduce new algorithms for combinations which have not been
studied. An extensive comparison is performed to evaluate properties of the
clustering algorithms in the context of instance segmentation in images,
including robustness to noise and efficiency. We show how one of the new
algorithms proposed in our framework outperforms all previously known
agglomerative methods for signed graphs, both on the competitive CREMI 2016 EM
segmentation benchmark and on the CityScapes dataset.Comment: 19 pages, 8 figures, 6 table
A Survey of Adaptive Resonance Theory Neural Network Models for Engineering Applications
This survey samples from the ever-growing family of adaptive resonance theory
(ART) neural network models used to perform the three primary machine learning
modalities, namely, unsupervised, supervised and reinforcement learning. It
comprises a representative list from classic to modern ART models, thereby
painting a general picture of the architectures developed by researchers over
the past 30 years. The learning dynamics of these ART models are briefly
described, and their distinctive characteristics such as code representation,
long-term memory and corresponding geometric interpretation are discussed.
Useful engineering properties of ART (speed, configurability, explainability,
parallelization and hardware implementation) are examined along with current
challenges. Finally, a compilation of online software libraries is provided. It
is expected that this overview will be helpful to new and seasoned ART
researchers
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