Results 1  10
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513
Sparse online greedy support vector regression
 13th European Conference on Machine Learning
, 2002
"... Abstract. We present a novel algorithm for sparse online greedy kernelbased nonlinear regression. This algorithm improves current approaches to kernelbased regression in two aspects. First, it operates online at each time step it observes a single new input sample, performs an update and discards ..."
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Cited by 45 (8 self)
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Abstract. We present a novel algorithm for sparse online greedy kernelbased nonlinear regression. This algorithm improves current approaches to kernelbased regression in two aspects. First, it operates online at each time step it observes a single new input sample, performs an update and discards
Stochastic online greedy learning with semibandit feedbacks.
 In Advances in Neural Information Processing Systems,
, 2015
"... Abstract The greedy algorithm is extensively studied in the field of combinatorial optimization for decades. In this paper, we address the online learning problem when the input to the greedy algorithm is stochastic with unknown parameters that have to be learned over time. We first propose the gre ..."
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Cited by 2 (2 self)
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Abstract The greedy algorithm is extensively studied in the field of combinatorial optimization for decades. In this paper, we address the online learning problem when the input to the greedy algorithm is stochastic with unknown parameters that have to be learned over time. We first propose
Learning to Order Things
 Journal of Artificial Intelligence Research
, 1998
"... There are many applications in which it is desirable to order rather than classify instances. Here we consider the problem of learning how to order, given feedback in the form of preference judgments, i.e., statements to the effect that one instance should be ranked ahead of another. We outline a ..."
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Cited by 409 (12 self)
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that the problem of finding the ordering that agrees best with a preference function is NPcomplete, even under very restrictive assumptions. Nevertheless, we describe a simple greedy algorithm that is guaranteed to find a good approximation. We then discuss an online learning algorithm, based on the "
Greedy Is an Almost Optimal Deque
, 2015
"... In this paper we extend the geometric binary search tree (BST) model of Demaine, Harmon, Iacono, Kane, and Pǎtraşcu (DHIKP) to accommodate for insertions and deletions. Within this extended model, we study the online Greedy BST algorithm introduced by DHIKP. Greedy BST is known to be equivalent ..."
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In this paper we extend the geometric binary search tree (BST) model of Demaine, Harmon, Iacono, Kane, and Pǎtraşcu (DHIKP) to accommodate for insertions and deletions. Within this extended model, we study the online Greedy BST algorithm introduced by DHIKP. Greedy BST is known to be equivalent
Greedy Online Bipartite Matching on Random Graphs
, 2013
"... We study the average performance of online greedy matching algorithms on G(n, n, p), the random bipartite graph with n vertices on each side and edges occurring independently with probability p = p(n). In the online model, vertices on one side of the graph are given up front while vertices on the ot ..."
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We study the average performance of online greedy matching algorithms on G(n, n, p), the random bipartite graph with n vertices on each side and edges occurring independently with probability p = p(n). In the online model, vertices on one side of the graph are given up front while vertices
Compressed suffix arrays and suffix trees with applications to text indexing and string matching
, 2005
"... The proliferation of online text, such as found on the World Wide Web and in online databases, motivates the need for spaceefficient text indexing methods that support fast string searching. We model this scenario as follows: Consider a text T consisting of n symbols drawn from a fixed alphabet Σ. ..."
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Cited by 239 (20 self)
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The proliferation of online text, such as found on the World Wide Web and in online databases, motivates the need for spaceefficient text indexing methods that support fast string searching. We model this scenario as follows: Consider a text T consisting of n symbols drawn from a fixed alphabet Σ
Greedy spectral embedding
"... Spectral dimensionality reduction methods and spectral clustering methods require computation of the principal eigenvectors of an n × n matrix where n is the number of examples. Following up on previously proposed techniques to speedup kernel methods by focusing on a subset of m examples, we study ..."
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Cited by 20 (2 self)
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a greedy selection procedure for this subset, based on the featurespace distance between a candidate example and the span of the previously chosen ones. In the case of kernel PCA or spectral clustering this reduces computation to O(m² n). For the same computational complexity, we can also compute
Optimal Greedy Diversity for Recommendation
"... The need for diversification manifests in various recommendation use cases. In this work, we propose a novel approach to diversifying a list of recommended items, which maximizes the utility of the items subject to the increase in their diversity. From a technical perspective, the problem can be v ..."
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be viewed as maximization of a modular function on the polytope of a submodular function, which can be solved optimally by a greedy method. We evaluate our approach in an offline analysis, which incorporates a number of baselines and metrics, and in two online user studies. In all the experiments, our
Greedy Rankings and Arank Numbers
, 2009
"... A ranking on a graph is an assignment of positive integers to its vertices such that any path between two vertices of the same rank contains a vertex of strictly larger rank. A ranking is locally minimal if reducing the rank of any single vertex produces a non ranking. A ranking is globally minimal ..."
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Cited by 1 (0 self)
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if reducing the ranks of any set of vertices produces a non ranking. A ranking is greedy if, for some ordering of the vertices, it is the ranking produced by assigning ranks in that order, always selecting the smallest possible rank. We will show that these three notions are equivalent. If a ranking satisfies
Reduced basis approximation and a posteriori error estimation for affinely parametrized elliptic coercive partial differential equations
, 2008
"... ... reduced basis approximation and a posteriori error estimation for linear functional outputs of affinely parametrized elliptic coercive partial differential equations. The essential ingredients are (primaldual) Galerkin projection onto a lowdimensional space associated with a smooth “parametric ..."
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Cited by 204 (37 self)
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“parametric manifold”—dimension reduction; efficient and effective greedy sampling methods for identification of optimal and numerically stable approximations—rapid convergence; a posteriori error estimation procedures—rigorous and sharp bounds for the linearfunctional outputs of interest; and OfflineOnline
Results 1  10
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513