Results 1 - 10 of 596

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
and optimizing the latent SVM objective function.

Pegasos: Primal Estimated sub-gradient solver for SVM

by Shai Shalev-Shwartz, Yoram Singer, Nathan Srebro, Andrew Cotter
"... We describe and analyze a simple and effective stochastic sub-gradient descent algorithm for solving the optimization problem cast by Support Vector Machines (SVM). We prove that the number of iterations required to obtain a solution of accuracy ɛ is Õ(1/ɛ), where each iteration operates on a singl ..."
Abstract - Cited by 542 (20 self) - Add to MetaCart
-linear kernels while working solely on the primal objective function, though in this case the runtime does depend linearly on the training set size. Our algorithm is particularly well suited for large text classification problems, where we demonstrate an order-of-magnitude speedup over previous SVM learning

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
training. We combine a margin-sensitive approach for data mining hard negative examples with a formalism we call latent SVM. A latent SVM, like a hidden CRF, leads to a non-convex training problem. However, a latent SVM is semi-convex and the training problem becomes convex once latent information

Training Products of Experts by Minimizing Contrastive Divergence

by Geoffrey E. Hinton , 2002
"... It is possible to combine multiple latent-variable models of the same data by multiplying their probability distributions together and then renormalizing. This way of combining individual “expert ” models makes it hard to generate samples from the combined model but easy to infer the values of the l ..."
Abstract - Cited by 850 (75 self) - Add to MetaCart
is unnecessary. Training a PoE by maximizing the likelihood of the data is difficult because it is hard even to approximate the derivatives of the renormalization term in the combination rule. Fortunately, a PoE can be trained using a different objective function called “contrastive divergence ” whose

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
variety of distance functions can be used and our experiments show state-of-the-art performance on a number of benchmark data sets for shape and texture classification (MNIST, USPS, CUReT) and object recognition (Caltech-101). On Caltech-101 we achieved a correct classification rate of 59

Latent Semantic Models for Collaborative filtering

by Thomas Hofmann - ACM Trans. Information Systems
"... Collaborative filtering aims at learning predictive models of user preferences, interests or behavior from community data, that is, a database of available user preferences. In this article, we describe a new family of model-based algorithms designed for this task. These algorithms rely on a statist ..."
Abstract - Cited by 331 (1 self) - Add to MetaCart
statistical modelling technique that introduces latent class variables in a mixture model setting to discover user communities and prototypical interest profiles. We investigate several variations to deal with discrete and continuous response variables as well as with different objective functions. The main

Efficient SVM training using low-rank kernel representations

by Shai Fine, Katya Scheinberg, Nello Cristianini, John Shawe-taylor, Bob Williamson - Journal of Machine Learning Research , 2001
"... SVM training is a convex optimization problem which scales with the training set size rather than the feature space dimension. While this is usually considered to be a desired quality, in large scale problems it may cause training to be impractical. The common techniques to handle this difficulty ba ..."
Abstract - Cited by 240 (3 self) - Add to MetaCart
on the change in the objective function value based on the approximation error and the number of active constraints (support vectors). This bound is general in the sense that it holds regardless of the approximation method.

Online Learning for Latent Dirichlet Allocation

by Matthew D. Hoffman, David M. Blei, Francis Bach
"... We develop an online variational Bayes (VB) algorithm for Latent Dirichlet Allocation (LDA). Online LDA is based on online stochastic optimization with a natural gradient step, which we show converges to a local optimum of the VB objective function. It can handily analyze massive document collection ..."
Abstract - Cited by 209 (21 self) - Add to MetaCart
We develop an online variational Bayes (VB) algorithm for Latent Dirichlet Allocation (LDA). Online LDA is based on online stochastic optimization with a natural gradient step, which we show converges to a local optimum of the VB objective function. It can handily analyze massive document

Probabilistic non-linear principal component analysis with Gaussian process latent variable models

by Neil Lawrence, Aapo Hyvärinen - Journal of Machine Learning Research , 2005
"... Summarising a high dimensional data set with a low dimensional embedding is a standard approach for exploring its structure. In this paper we provide an overview of some existing techniques for discovering such embeddings. We then introduce a novel probabilistic interpretation of principal component ..."
Abstract - Cited by 229 (24 self) - Add to MetaCart
analysis of the GP-LVM objective function, we relate the model to popular spectral techniques such as kernel PCA and multidimensional scaling. We then review a practical algorithm for GP-LVMs in the context of large data sets and develop it to also handle discrete valued data and missing attributes. We

Latent SVM for Object Localization in Weakly Labeled Videos

by Mrigank Rochan, Yang Wang
"... Abstract—We consider the problem of object localization in Internet videos. An Internet video (e.g. YouTube videos) is often associated with a semantic label (also known as a tag) describing the main object present within it. However, the tag does not provide any spatial or temporal information abou ..."
Abstract - Add to MetaCart
about the main object in the video. Such videos are weakly labeled. Given weakly labeled video with video-level object class tags, our goal is to learn a model that can be used to localize the objects in other videos with such tags. We define a latent SVM based learning framework to tackle this problem
Results 1 - 10 of 596