Results 261 to 270 of about 6,473,960 (319)
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A Suboptimal Embedding Algorithm for Binary Matrix Embedding
2012 International Symposium on Computer, Consumer and Control, 2012A novel sub optimal hiding algorithm for binary data based on iterative searching embedding, ISE, is proposed. In most cases, an ML algorithm is criticized for being extremely sensitive to the dimension (n-m), due to the fact that the operation complexity varies exponentially with (n-m).
Jyun-Jie Wang +3 more
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Improved algorithms for optimal embeddings
ACM Transactions on Algorithms, 2008In the last decade, the notion of metric embeddings with small distortion has received wide attention in the literature, with applications in combinatorial optimization, discrete mathematics, and bio-informatics. The notion of embedding is, given two metric spaces on the same number of points, to find a bijection that minimizes maximum Lipschitz and bi-
Nishanth Chandran +5 more
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A novel watermark embedding algorithm
Proceedings. International Conference on Machine Learning and Cybernetics, 2003The recent growth of networked multimedia systems has caused the need for intellectual property rights, like images, music and movies. One approach to solve this problem is to add an invisible image to an image that can be used to provide solid proofs of ownership. These invisible images or signatures are known as digital watermarks.
null Zhuo Zhao, null Neng-Hai Yu
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Discriminative Fisher Embedding Dictionary Learning Algorithm for Object Recognition
IEEE Transactions on Neural Networks and Learning Systems, 2020Both interclass variances and intraclass similarities are crucial for improving the classification performance of discriminative dictionary learning (DDL) algorithms.
Zhengmin Li +4 more
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Embedding optimisation algorithms with Mosel
Quarterly Journal of the Belgian, French and Italian Operations Research Societies, 2003zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Ciriani, Tito A. +2 more
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Distributed Algorithms for Graph Embedding
2022Due to the continuously tendency to model domains in a network format, network representation learning (NRL) is a field where a lot of research has been carried out the recent years. Programmatically, a network is preserved in an adjacency matrix that preserves the neighbors for each network’s node.
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1995
The complexity of embedding a graph into a variety of topological surfaces is investigated. A new data structure for graph embeddings is introduced and shown to be superior to the previously known data structures. In particular, the new data structure efficiently supports all on-line operations for general graph embeddings.
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The complexity of embedding a graph into a variety of topological surfaces is investigated. A new data structure for graph embeddings is introduced and shown to be superior to the previously known data structures. In particular, the new data structure efficiently supports all on-line operations for general graph embeddings.
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Incomplete hypercubes: Algorithms and embeddings
The Journal of Supercomputing, 1994The hypercube, though a popular and versatile architecture, has a major drawback in that its size must be a power of two. In order to alleviate this drawback, Katseff [1988] defined theincomplete hypercube, which allows a hypercube-like architecture to be defined for any number of nodes.
Alfred J. Boals +2 more
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Chaos Embedded Metaheuristic Algorithms
2014In nature complex biological phenomena such as the collective behavior of birds, foraging activity of bees or cooperative behavior of ants may result from relatively simple rules which however present nonlinear behavior being sensitive to initial conditions.
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Enhanced Snake algorithm by embedded domain transformation
Pattern Recognition, 2006zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Lam, S. Y., Tong, C. S.
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