• Publications
  • Influence
Estimating the Support of a High-Dimensional Distribution
The algorithm is a natural extension of the support vector algorithm to the case of unlabeled data by carrying out sequential optimization over pairs of input patterns and providing a theoretical analysis of the statistical performance of the algorithm. Expand
Fast training of support vector machines using sequential minimal optimization, advances in kernel methods
SMO breaks this large quadratic programming problem into a series of smallest possible QP problems, which avoids using a time-consuming numerical QP optimization as an inner loop and hence SMO is fastest for linear SVMs and sparse data sets. Expand
Support Vector Method for Novelty Detection
The algorithm is a natural extension of the support vector algorithm to the case of unlabelled data and is regularized by controlling the length of the weight vector in an associated feature space. Expand
Large Margin DAGs for Multiclass Classification
An algorithm, DAGSVM, is presented, which operates in a kernel-induced feature space and uses two-class maximal margin hyperplanes at each decision-node of the DDAG, which is substantially faster to train and evaluate than either the standard algorithm or Max Wins, while maintaining comparable accuracy to both of these algorithms. Expand
Best practices for convolutional neural networks applied to visual document analysis
A set of concrete bestpractices that document analysis researchers can use to get good results with neural networks, including a simple "do-it-yourself" implementation of convolution with a flexible architecture suitable for many visual document problems. Expand
A Resource-Allocating Network for Function Interpolation
  • John C. Platt
  • Medicine, Computer Science
  • Neural Computation
  • 1 June 1991
A network that allocates a new computational unit whenever an unusual pattern is presented to the network, which learns much faster than do those using backpropagation networks and uses a comparable number of synapses. Expand
Multiple Instance Boosting for Object Detection
MILBoost adapts the feature selection criterion of MILBoost to optimize the performance of the Viola-Jones cascade to show the advantage of simultaneously learning the locations and scales of the objects in the training set along with the parameters of the classifier. Expand
From captions to visual concepts and back
This paper uses multiple instance learning to train visual detectors for words that commonly occur in captions, including many different parts of speech such as nouns, verbs, and adjectives, and develops a maximum-entropy language model. Expand
Elastically deformable models
The description of shape and the description of motion are unified and differential equations that model the behavior of non-rigid curves, surfaces, and solids as a function of time are constructed. Expand
Quantum supremacy using a programmable superconducting processor
Quantum supremacy is demonstrated using a programmable superconducting processor known as Sycamore, taking approximately 200 seconds to sample one instance of a quantum circuit a million times, which would take a state-of-the-art supercomputer around ten thousand years to compute. Expand