Results 1 - 10 of 2,538

Object Detection with Discriminatively Trained Part Based Models

by Pedro F. Felzenszwalb, Ross B. Girshick, David McAllester, Deva Ramanan
"... We describe an object detection system based on mixtures of multiscale deformable part models. Our system is able to represent highly variable object classes and achieves state-of-the-art results in the PASCAL object detection challenges. While deformable part models have become quite popular, their ..."
Abstract - Cited by 1422 (49 self) - Add to MetaCart
, their value had not been demonstrated on difficult benchmarks such as the PASCAL datasets. Our system relies on new methods for discriminative training with partially labeled data. We combine a margin-sensitive approach for data-mining hard negative examples with a formalism we call latent SVM. A latent SVM

A discriminatively trained, multiscale, deformable part model

by Pedro Felzenszwalb, David Mcallester, Deva Ramanan - In IEEE Conference on Computer Vision and Pattern Recognition (CVPR-2008 , 2008
"... This paper describes a discriminatively trained, multiscale, deformable part model for object detection. Our system achieves a two-fold improvement in average precision over the best performance in the 2006 PASCAL person detection challenge. It also outperforms the best results in the 2007 challenge ..."
Abstract - Cited by 555 (11 self) - Add to MetaCart
This paper describes a discriminatively trained, multiscale, deformable part model for object detection. Our system achieves a two-fold improvement in average precision over the best performance in the 2006 PASCAL person detection challenge. It also outperforms the best results in the 2007

Particle swarm optimization

by James Kennedy, Russell Eberhart , 1995
"... A concept for the optimization of nonlinear functions using particle swarm methodology is introduced. The evolution of several paradigms is outlined, and an implementation of one of the paradigms is discussed. Benchmark testing of the paradigm is described, and applications, including nonlinear fun ..."
Abstract - Cited by 3769 (22 self) - Add to MetaCart
A concept for the optimization of nonlinear functions using particle swarm methodology is introduced. The evolution of several paradigms is outlined, and an implementation of one of the paradigms is discussed. Benchmark testing of the paradigm is described, and applications, including nonlinear

Fast Effective Rule Induction

by William W. Cohen , 1995
"... Many existing rule learning systems are computationally expensive on large noisy datasets. In this paper we evaluate the recently-proposed rule learning algorithm IREP on a large and diverse collection of benchmark problems. We show that while IREP is extremely efficient, it frequently gives error r ..."
Abstract - Cited by 1274 (21 self) - Add to MetaCart
on 22 of 37 benchmark problems, scales nearly linearly with the number of training examples, and can efficiently process noisy datasets containing hundreds of thousands of examples.

Linear spatial pyramid matching using sparse coding for image classification

by Jianchao Yang, Kai Yu, Yihong Gong, Thomas Huang - in IEEE Conference on Computer Vision and Pattern Recognition(CVPR , 2009
"... Recently SVMs using spatial pyramid matching (SPM) kernel have been highly successful in image classification. Despite its popularity, these nonlinear SVMs have a complexity O(n 2 ∼ n 3) in training and O(n) in testing, where n is the training size, implying that it is nontrivial to scaleup the algo ..."
Abstract - Cited by 497 (21 self) - Add to MetaCart
Recently SVMs using spatial pyramid matching (SPM) kernel have been highly successful in image classification. Despite its popularity, these nonlinear SVMs have a complexity O(n 2 ∼ n 3) in training and O(n) in testing, where n is the training size, implying that it is nontrivial to scaleup

Svm-knn: Discriminative nearest neighbor classification for visual category recognition

by Hao Zhang, Alexander C. Berg, Michael Maire, Jitendra Malik - in CVPR , 2006
"... We consider visual category recognition in the framework of measuring similarities, or equivalently perceptual distances, to prototype examples of categories. This approach is quite flexible, and permits recognition based on color, texture, and particularly shape, in a homogeneous framework. While n ..."
Abstract - Cited by 342 (10 self) - Add to MetaCart
. We propose a hybrid of these two methods which deals naturally with the multiclass setting, has reasonable computational complexity both in training and at run time, and yields excellent results in practice. The basic idea is to find close neighbors to a query sample and train a local support vector

PROBEN1 - a set of neural network benchmark problems and benchmarking rules

by Lutz Prechelt , 1994
"... Proben1 is a collection of problems for neural network learning in the realm of pattern classification and function approximation plus a set of rules and conventions for carrying out benchmark tests with these or similar problems. Proben1 contains 15 data sets from 12 different domains. All datasets ..."
Abstract - Cited by 234 (0 self) - Add to MetaCart
Proben1 is a collection of problems for neural network learning in the realm of pattern classification and function approximation plus a set of rules and conventions for carrying out benchmark tests with these or similar problems. Proben1 contains 15 data sets from 12 different domains. All

Leave-One-Out Support Vector Machines

by Jason Weston , 1999
"... We present a new learning algorithm for pattern recognition inspired by a recent upper bound on leave--one--out error [ Jaakkola and Haussler, 1999 ] proved for Support Vector Machines (SVMs) [ Vapnik, 1995; 1998 ] . The new approach directly minimizes the expression given by the bound in an attempt ..."
Abstract - Cited by 301 (5 self) - Add to MetaCart
of kernel, it is parameterless -- the selection of the number of training errors is inherent in the algorithm and not chosen by an extra free parameter as in SVMs. First experiments using the method on benchmark datasets from the UCI repository show results similar to SVMs which have been tuned to have

Efficient Path Profiling

by Thomas Ball, James R. Larus - In Proceedings of the 29th Annual International Symposium on Microarchitecture , 1996
"... A path profile determines how many times each acyclic path in a routine executes. This type of profiling subsumes the more common basic block and edge profiling, which only approximate path frequencies. Path profiles have many potential uses in program performance tuning, profile-directed compilatio ..."
Abstract - Cited by 287 (8 self) - Add to MetaCart
benchmarks, path profiling overhead averaged 31%, as compared to 16 % for efficient edge profiling. Path profiling also identifies longer paths than a previous technique, which predicted paths from edge profiles (average of 88, versus 34 instructions). Moreover, profiling shows that the SPEC95 train input

Deep Neural Networks for Acoustic Modeling in Speech Recognition

by Geoffrey Hinton, Li Deng, Dong Yu, George Dahl, Abdel-rahman Mohamed, Navdeep Jaitly, Vincent Vanhoucke, Patrick Nguyen, Tara Sainath, Brian Kingsbury
"... Most current speech recognition systems use hidden Markov models (HMMs) to deal with the temporal variability of speech and Gaussian mixture models to determine how well each state of each HMM fits a frame or a short window of frames of coefficients that represents the acoustic input. An alternative ..."
Abstract - Cited by 272 (47 self) - Add to MetaCart
. An alternative way to evaluate the fit is to use a feedforward neural network that takes several frames of coefficients as input and produces posterior probabilities over HMM states as output. Deep neural networks with many hidden layers, that are trained using new methods have been shown to outperform Gaussian
Results 1 - 10 of 2,538