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597,879
MaximumMargin Matrix Factorization
 Advances in Neural Information Processing Systems 17
, 2005
"... We present a novel approach to collaborative prediction, using lownorm instead of lowrank factorizations. The approach is inspired by, and has strong connections to, largemargin linear discrimination. We show how to learn lownorm factorizations by solving a semidefinite program, and discuss ..."
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Cited by 263 (21 self)
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We present a novel approach to collaborative prediction, using lownorm instead of lowrank factorizations. The approach is inspired by, and has strong connections to, largemargin linear discrimination. We show how to learn lownorm factorizations by solving a semidefinite program
Maximum margin planning
 IN PROCEEDINGS OF THE 23RD INTERNATIONAL CONFERENCE ON MACHINE LEARNING (ICML’06
, 2006
"... Imitation learning of sequential, goaldirected behavior by standard supervised techniques is often difficult. We frame learning such behaviors as a maximum margin structured prediction problem over a space of policies. In this approach, we learn mappings from features to cost so an optimal policy in ..."
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Cited by 145 (28 self)
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Imitation learning of sequential, goaldirected behavior by standard supervised techniques is often difficult. We frame learning such behaviors as a maximum margin structured prediction problem over a space of policies. In this approach, we learn mappings from features to cost so an optimal policy
Maximum margin clustering
 Advances in Neural Information Processing Systems 17
, 2005
"... We propose a new method for clustering based on finding maximum margin hyperplanes through data. By reformulating the problem in terms of the implied equivalence relation matrix, we can pose the problem as a convex integer program. Although this still yields a difficult computational problem, the ha ..."
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Cited by 135 (4 self)
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We propose a new method for clustering based on finding maximum margin hyperplanes through data. By reformulating the problem in terms of the implied equivalence relation matrix, we can pose the problem as a convex integer program. Although this still yields a difficult computational problem
Fast maximum margin matrix factorization for collaborative prediction
 In Proceedings of the 22nd International Conference on Machine Learning (ICML
, 2005
"... Maximum Margin Matrix Factorization (MMMF) was recently suggested (Srebro et al., 2005) as a convex, infinite dimensional alternative to lowrank approximations and standard factor models. MMMF can be formulated as a semidefinite programming (SDP) and learned using standard SDP solvers. However, cu ..."
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Cited by 247 (6 self)
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Maximum Margin Matrix Factorization (MMMF) was recently suggested (Srebro et al., 2005) as a convex, infinite dimensional alternative to lowrank approximations and standard factor models. MMMF can be formulated as a semidefinite programming (SDP) and learned using standard SDP solvers. However
Maximum Margin Planning Maximum Margin Planning
"... Mobile robots often rely upon systems that render sensor data and perceptual features into costs that can be used in a planner. The behavior that a designer wishes the planner to execute is often clear, while specifying costs that engender this behavior is a much more difficult task. This is particu ..."
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. This is particularly apparent when attempting to simultaneously tune many parameters that define the mapping from features to resulting plans. We provide a novel, structured maximum margin approach to learning based on example trajectories demonstrated by a human. The learning problem is transformed into a convex
The Relaxed Online Maximum Margin Algorithm
 Machine Learning
, 2000
"... We describe a new incremental algorithm for training linear threshold functions: the Relaxed Online Maximum Margin Algorithm, or ROMMA. ROMMA can be viewed as an approximation to the algorithm that repeatedly chooses the hyperplane that classifies previously seen examples correctly with the maximum ..."
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Cited by 84 (1 self)
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We describe a new incremental algorithm for training linear threshold functions: the Relaxed Online Maximum Margin Algorithm, or ROMMA. ROMMA can be viewed as an approximation to the algorithm that repeatedly chooses the hyperplane that classifies previously seen examples correctly with the maximum
Hierarchical MaximumMargin Clustering
"... We present a hierarchical maximummargin clustering method for unsupervised data analysis. Our method extends beyond flat maximummargin clustering, and performs clustering recursively in a topdown manner. We propose an effective greedy splitting criteria for selecting which cluster to split next ..."
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We present a hierarchical maximummargin clustering method for unsupervised data analysis. Our method extends beyond flat maximummargin clustering, and performs clustering recursively in a topdown manner. We propose an effective greedy splitting criteria for selecting which cluster to split
Maximum Margin Temporal Clustering
"... Temporal Clustering (TC) refers to the factorization of multiple time series into a set of nonoverlapping segments that belong to k temporal clusters. Existing methods based on extensions of generative models such as kmeans or Switching Linear Dynamical Systems (SLDS) often lead to intractable inf ..."
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Cited by 3 (0 self)
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inference and lack a mechanism for feature selection, critical when dealing with high dimensional data. To overcome these limitations, this paper proposes Maximum Margin Temporal Clustering (MMTC). MMTC simultaneously determines the start and the end of each segment, while learning a multiclass Support
Random maximum margin hashing
 In CVPR
, 2011
"... Following the success of hashing methods for multidimensional indexing, more and more works are interested in embedding visual feature space in compact hash codes. Such approaches are not an alternative to using index structures but a complementary way to reduce both the memory usage and the dist ..."
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Cited by 29 (5 self)
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Random Maximum Margin Hashing scheme (RMMH) outperforms four stateoftheart hashing methods, notably in kernel spaces. 1 1.
Maximum Margin Clustering
"... We propose a new method for clustering based on finding maximum margin hyperplanes through data. By reformulating the problem in terms of the implied equivalence relation matrix, we can pose the problem as a convex integer program. Although this still yields a difficult computational problem, the ..."
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We propose a new method for clustering based on finding maximum margin hyperplanes through data. By reformulating the problem in terms of the implied equivalence relation matrix, we can pose the problem as a convex integer program. Although this still yields a difficult computational problem
Results 1  10
of
597,879