The Experts below are selected from a list of 25170 Experts worldwide ranked by ideXlab platform
Roy Schwartz - One of the best experts on this subject based on the ideXlab platform.
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a tight linear time 1 2 approximation for unconstrained submodular maximization
SIAM Journal on Computing, 2015Co-Authors: Niv Buchbinder, Moran Feldman, Joseph Naor, Roy SchwartzAbstract:We consider the \sf Unconstrained Submodular Maximization problem in which we are given a nonnegative submodular function $f:2^{\mathcal{N}}\rightarrow \mathbb{R}^+$, and the objective is to find a subset $S\subseteq \mathcal{N}$ maximizing $f(S)$. This is one of the most basic submodular optimization problems, having a wide range of applications. Some well-known problems captured by \sf Unconstrained Submodular Maximization include \sf Max-Cut, \sf Max-DiCut, and variants of \sf Max-SAT and maximum facility location. We present a simple randomized linear time algorithm achieving a tight approximation guarantee of 1/2, thus matching the known hardness result of Feige, Mirrokni, and Vondrak [SIAM J. Comput., 40 (2011), pp. 1133--1153]. Our algorithm is based on an adaptation of the Greedy Approach which exploits certain symmetry properties of the problem.
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a tight linear time 1 2 approximation for unconstrained submodular maximization
Foundations of Computer Science, 2012Co-Authors: Niv Buchbinder, Moran Feldman, Joseph Naor, Roy SchwartzAbstract:We consider the Unconstrained Sub modular Maximization problem in which we are given a non-negative sub modular function $f:2^{N}\right arrow R^+$, and the objective is to find a subset $S\subseteq N$ maximizing $f(S)$. This is one of the most basic sub modular optimization problems, having a wide range of applications. Some well known problems captured by Unconstrained Sub modular Maximization include Max-Cut, Max-DiCut, and variants of Max-SAT and maximum facility location. We present a simple randomized linear time algorithm achieving a tight approximation guarantee of $1/2$, thus matching the known hardness result of Feige et al. Our algorithm is based on an adaptation of the Greedy Approach which exploits certain symmetry properties of the problem. Our method might seem counterintuitive, since it is known that the Greedy algorithm fails to achieve any bounded approximation factor for the problem.
Niv Buchbinder - One of the best experts on this subject based on the ideXlab platform.
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a tight linear time 1 2 approximation for unconstrained submodular maximization
SIAM Journal on Computing, 2015Co-Authors: Niv Buchbinder, Moran Feldman, Joseph Naor, Roy SchwartzAbstract:We consider the \sf Unconstrained Submodular Maximization problem in which we are given a nonnegative submodular function $f:2^{\mathcal{N}}\rightarrow \mathbb{R}^+$, and the objective is to find a subset $S\subseteq \mathcal{N}$ maximizing $f(S)$. This is one of the most basic submodular optimization problems, having a wide range of applications. Some well-known problems captured by \sf Unconstrained Submodular Maximization include \sf Max-Cut, \sf Max-DiCut, and variants of \sf Max-SAT and maximum facility location. We present a simple randomized linear time algorithm achieving a tight approximation guarantee of 1/2, thus matching the known hardness result of Feige, Mirrokni, and Vondrak [SIAM J. Comput., 40 (2011), pp. 1133--1153]. Our algorithm is based on an adaptation of the Greedy Approach which exploits certain symmetry properties of the problem.
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a tight linear time 1 2 approximation for unconstrained submodular maximization
Foundations of Computer Science, 2012Co-Authors: Niv Buchbinder, Moran Feldman, Joseph Naor, Roy SchwartzAbstract:We consider the Unconstrained Sub modular Maximization problem in which we are given a non-negative sub modular function $f:2^{N}\right arrow R^+$, and the objective is to find a subset $S\subseteq N$ maximizing $f(S)$. This is one of the most basic sub modular optimization problems, having a wide range of applications. Some well known problems captured by Unconstrained Sub modular Maximization include Max-Cut, Max-DiCut, and variants of Max-SAT and maximum facility location. We present a simple randomized linear time algorithm achieving a tight approximation guarantee of $1/2$, thus matching the known hardness result of Feige et al. Our algorithm is based on an adaptation of the Greedy Approach which exploits certain symmetry properties of the problem. Our method might seem counterintuitive, since it is known that the Greedy algorithm fails to achieve any bounded approximation factor for the problem.
Steven L Salzberg - One of the best experts on this subject based on the ideXlab platform.
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lookahead and pathology in decision tree induction
International Joint Conference on Artificial Intelligence, 1995Co-Authors: Sreerama K Murthy, Steven L SalzbergAbstract:The standard Approach to decision tree induction is a top-down Greedy agorithm that makes locally optimal irrevocable decisions at each node of a tree. In this paper we empircally study an alternative Approach in which the algorithms use one-level lookahead to decide what test to use at a node. we systematically compare using a very large number of artificial data sets the quality of dimension trees induced by the Greedy Approach to that of trees induced using lookahead. The main observations from our experiments are (1) the Greedy Approach consistently produced trees that were just as at accurate as trees produced with the much more expensive lookahead step and (n) we observed many instances of pathology, i.e, lookahead produced trees that were both larger and less accurate than trees produced without it.
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decision tree induction how effective is the Greedy heuristic
Knowledge Discovery and Data Mining, 1995Co-Authors: Sreerama K Murthy, Steven L SalzbergAbstract:Most existing decision tree systems use a Greedy Approach to induce trees — locally optimal splits are induced at every node of the tree. Although the Greedy Approach is suboptimal, it is believed to produce reasonably good trees. In the current work, we attempt to verify this belief. We quantify the goodness of Greedy tree induction empirically, using the popular decision tree algorithms, C4.5 and CART. We induce decision trees on thousands of synthetic data sets and compare them to the corresponding optimal trees, which in turn are found using a novel map coloring idea. We measure the effect on Greedy induction of variables such as the underlying concept complexity, training set size, noise and dimensionality. Our experiments show, among other things, that the expected classification cost of a greedily induced tree is consistently very close to that of the optimal tree.
Joseph Naor - One of the best experts on this subject based on the ideXlab platform.
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a tight linear time 1 2 approximation for unconstrained submodular maximization
SIAM Journal on Computing, 2015Co-Authors: Niv Buchbinder, Moran Feldman, Joseph Naor, Roy SchwartzAbstract:We consider the \sf Unconstrained Submodular Maximization problem in which we are given a nonnegative submodular function $f:2^{\mathcal{N}}\rightarrow \mathbb{R}^+$, and the objective is to find a subset $S\subseteq \mathcal{N}$ maximizing $f(S)$. This is one of the most basic submodular optimization problems, having a wide range of applications. Some well-known problems captured by \sf Unconstrained Submodular Maximization include \sf Max-Cut, \sf Max-DiCut, and variants of \sf Max-SAT and maximum facility location. We present a simple randomized linear time algorithm achieving a tight approximation guarantee of 1/2, thus matching the known hardness result of Feige, Mirrokni, and Vondrak [SIAM J. Comput., 40 (2011), pp. 1133--1153]. Our algorithm is based on an adaptation of the Greedy Approach which exploits certain symmetry properties of the problem.
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a tight linear time 1 2 approximation for unconstrained submodular maximization
Foundations of Computer Science, 2012Co-Authors: Niv Buchbinder, Moran Feldman, Joseph Naor, Roy SchwartzAbstract:We consider the Unconstrained Sub modular Maximization problem in which we are given a non-negative sub modular function $f:2^{N}\right arrow R^+$, and the objective is to find a subset $S\subseteq N$ maximizing $f(S)$. This is one of the most basic sub modular optimization problems, having a wide range of applications. Some well known problems captured by Unconstrained Sub modular Maximization include Max-Cut, Max-DiCut, and variants of Max-SAT and maximum facility location. We present a simple randomized linear time algorithm achieving a tight approximation guarantee of $1/2$, thus matching the known hardness result of Feige et al. Our algorithm is based on an adaptation of the Greedy Approach which exploits certain symmetry properties of the problem. Our method might seem counterintuitive, since it is known that the Greedy algorithm fails to achieve any bounded approximation factor for the problem.
Moran Feldman - One of the best experts on this subject based on the ideXlab platform.
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a tight linear time 1 2 approximation for unconstrained submodular maximization
SIAM Journal on Computing, 2015Co-Authors: Niv Buchbinder, Moran Feldman, Joseph Naor, Roy SchwartzAbstract:We consider the \sf Unconstrained Submodular Maximization problem in which we are given a nonnegative submodular function $f:2^{\mathcal{N}}\rightarrow \mathbb{R}^+$, and the objective is to find a subset $S\subseteq \mathcal{N}$ maximizing $f(S)$. This is one of the most basic submodular optimization problems, having a wide range of applications. Some well-known problems captured by \sf Unconstrained Submodular Maximization include \sf Max-Cut, \sf Max-DiCut, and variants of \sf Max-SAT and maximum facility location. We present a simple randomized linear time algorithm achieving a tight approximation guarantee of 1/2, thus matching the known hardness result of Feige, Mirrokni, and Vondrak [SIAM J. Comput., 40 (2011), pp. 1133--1153]. Our algorithm is based on an adaptation of the Greedy Approach which exploits certain symmetry properties of the problem.
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a tight linear time 1 2 approximation for unconstrained submodular maximization
Foundations of Computer Science, 2012Co-Authors: Niv Buchbinder, Moran Feldman, Joseph Naor, Roy SchwartzAbstract:We consider the Unconstrained Sub modular Maximization problem in which we are given a non-negative sub modular function $f:2^{N}\right arrow R^+$, and the objective is to find a subset $S\subseteq N$ maximizing $f(S)$. This is one of the most basic sub modular optimization problems, having a wide range of applications. Some well known problems captured by Unconstrained Sub modular Maximization include Max-Cut, Max-DiCut, and variants of Max-SAT and maximum facility location. We present a simple randomized linear time algorithm achieving a tight approximation guarantee of $1/2$, thus matching the known hardness result of Feige et al. Our algorithm is based on an adaptation of the Greedy Approach which exploits certain symmetry properties of the problem. Our method might seem counterintuitive, since it is known that the Greedy algorithm fails to achieve any bounded approximation factor for the problem.
