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2,711
Maxmargin Markov networks
, 2003
"... In typical classification tasks, we seek a function which assigns a label to a single object. Kernelbased approaches, such as support vector machines (SVMs), which maximize the margin of confidence of the classifier, are the method of choice for many such tasks. Their popularity stems both from the ..."
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Cited by 604 (15 self)
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the ability to use highdimensional feature spaces, and from their strong theoretical guarantees. However, many realworld tasks involve sequential, spatial, or structured data, where multiple labels must be assigned. Existing kernelbased methods ignore structure in the problem, assigning labels
Randomized kinodynamic planning
 THE INTERNATIONAL JOURNAL OF ROBOTICS RESEARCH 2001; 20; 378
, 2001
"... This paper presents the first randomized approach to kinodynamic planning (also known as trajectory planning or trajectory design). The task is to determine control inputs to drive a robot from an initial configuration and velocity to a goal configuration and velocity while obeying physically based ..."
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Cited by 626 (35 self)
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dynamical models and avoiding obstacles in the robot’s environment. The authors consider generic systems that express the nonlinear dynamics of a robot in terms of the robot’s highdimensional configuration space. Kinodynamic planning is treated as a motionplanning problem in a higher dimensional state
Gradientbased learning applied to document recognition
 Proceedings of the IEEE
, 1998
"... Multilayer neural networks trained with the backpropagation algorithm constitute the best example of a successful gradientbased learning technique. Given an appropriate network architecture, gradientbased learning algorithms can be used to synthesize a complex decision surface that can classify hi ..."
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Cited by 1533 (84 self)
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highdimensional patterns, such as handwritten characters, with minimal preprocessing. This paper reviews various methods applied to handwritten character recognition and compares them on a standard handwritten digit recognition task. Convolutional neural networks, which are specifically designed
The Complete Atomic Structure of the Large Ribosomal Subunit at 2.4 Å Resolution
 Science
, 2000
"... ation, and termination phases of protein synthesis. Because the structures of several DNA and RNA polymerases have been determined at atomic resolution, the mechanisms of DNA and RNA synthesis are both well understood. Determination of the structure of the ribosome, however, has proven a daunting t ..."
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Cited by 539 (13 self)
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mystery. Electron microscopy has contributed to our understanding of ribosome structure ever since the ribosome was discovered. In the last few years, threedimensional (3D) electron microscopic images of the ribosome have been produced at resolutions sufficiently high to visualize many of the proteins
RealTime Computing Without Stable States: A New Framework for Neural Computation Based on Perturbations
"... A key challenge for neural modeling is to explain how a continuous stream of multimodal input from a rapidly changing environment can be processed by stereotypical recurrent circuits of integrateandfire neurons in realtime. We propose a new computational model for realtime computing on timevar ..."
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Cited by 469 (38 self)
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varying input that provides an alternative to paradigms based on Turing machines or attractor neural networks. It does not require a taskdependent construction of neural circuits. Instead it is based on principles of high dimensional dynamical systems in combination with statistical learning theory, and can
Interactive HighDimensional Data Visualization
 Journal of Computational and Graphical Statistics
, 1996
"... We propose a rudimentary taxonomy of interactive data visualization based on a triad of data analytic tasks: finding Gestalt, posing queries, and making comparisons. These tasks are supported by three classes of nteractive view manipulation: focusing, linking and arranging views. This discussion ext ..."
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Cited by 137 (18 self)
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and arranging views, namely: highdimensional projections, linked scatterplot brusing, and matrices of conditional plots.
Using mutual information for selecting features in supervised neural net learning
 IEEE TRANSACTIONS ON NEURAL NETWORKS
, 1994
"... This paper investigates the application of the mutual infor“ criterion to evaluate a set of candidate features and to select an informative subset to be used as input data for a neural network classifier. Because the mutual information measures arbitrary dependencies between random variables, it is ..."
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Cited by 358 (1 self)
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, the use of the mutual information for tasks characterized by high input dimensionality requires suitable approximations because of the prohibitive demands on computation and samples. An algorithm is proposed that is based on a “greedy” selection of the features and that takes both the mutual information
Highdimensional data and the Lasso
, 2013
"... How would you try to solve a linear system of equations with more unknowns than equations? Of course, there are infinitely many solutions, and yet this is the sort of the problem statisticians face with many modern datasets, arising in genetics, imaging, finance and many other fields. What’s worse, ..."
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. This is an example of regression analysis, one of the most important tasks in Statistics. Often, we may assume that the unknown regression function is linear in the predictors, giving the following mathematical formulation of the problem: Y = Xβ + ε, (0.1) where Y ∈ Rn is the vector of responses; X ∈ Rn
Convolution Kernels for Natural Language
 Advances in Neural Information Processing Systems 14
, 2001
"... We describe the application of kernel methods to Natural Language Processing (NLP) problems. In many NLP tasks the objects being modeled are strings, trees, graphs or other discrete structures which require some mechanism to convert them into feature vectors. We describe kernels for various natural ..."
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Cited by 340 (7 self)
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language structures, allowing rich, high dimensional representations of these structures. We show how a kernel over trees can be applied to parsing using the voted perceptron algorithm, and we give experimental results on the ATIS corpus of parse trees.
Margin trees for highdimensional classification
 Journal of Machine Learning Research
"... We propose a method for the classification of more than two classes, from highdimensional features. Our approach is to build a binary decision tree in a topdown manner, using the optimal margin classifier at each split. We implement an exact greedy algorithm for this task, and compare its performa ..."
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Cited by 20 (0 self)
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We propose a method for the classification of more than two classes, from highdimensional features. Our approach is to build a binary decision tree in a topdown manner, using the optimal margin classifier at each split. We implement an exact greedy algorithm for this task, and compare its
Results 1  10
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2,711