Results 1  10
of
34,920
Large Margin Classification Using the Perceptron Algorithm
 Machine Learning
, 1998
"... We introduce and analyze a new algorithm for linear classification which combines Rosenblatt 's perceptron algorithm with Helmbold and Warmuth's leaveoneout method. Like Vapnik 's maximalmargin classifier, our algorithm takes advantage of data that are linearly separable with large ..."
Abstract

Cited by 518 (2 self)
 Add to MetaCart
We introduce and analyze a new algorithm for linear classification which combines Rosenblatt 's perceptron algorithm with Helmbold and Warmuth's leaveoneout method. Like Vapnik 's maximalmargin classifier, our algorithm takes advantage of data that are linearly separable
Discriminative Training Methods for Hidden Markov Models: Theory and Experiments with Perceptron Algorithms
, 2002
"... We describe new algorithms for training tagging models, as an alternative to maximumentropy models or conditional random fields (CRFs). The algorithms rely on Viterbi decoding of training examples, combined with simple additive updates. We describe theory justifying the algorithms through a modific ..."
Abstract

Cited by 641 (16 self)
 Add to MetaCart
modification of the proof of convergence of the perceptron algorithm for classification problems. We give experimental results on partofspeech tagging and base noun phrase chunking, in both cases showing improvements over results for a maximumentropy tagger.
Incremental parsing with the perceptron algorithm
 In ACL
, 2004
"... This paper describes an incremental parsing approach where parameters are estimated using a variant of the perceptron algorithm. A beamsearch algorithm is used during both training and decoding phases of the method. The perceptron approach was implemented with the same feature set as that of an exi ..."
Abstract

Cited by 174 (4 self)
 Add to MetaCart
This paper describes an incremental parsing approach where parameters are estimated using a variant of the perceptron algorithm. A beamsearch algorithm is used during both training and decoding phases of the method. The perceptron approach was implemented with the same feature set
A smooth perceptron algorithm
 SIAM Journal on Optimization
, 2012
"... The perceptron algorithm, introduced in the late fifties in the machine learning community, is a simple greedy algorithm for finding a solution to a finite set of linear inequalities. The algorithm’s main advantages are its simplicity and noise tolerance. The algorithm’s main disadvantage is its slo ..."
Abstract

Cited by 6 (1 self)
 Add to MetaCart
The perceptron algorithm, introduced in the late fifties in the machine learning community, is a simple greedy algorithm for finding a solution to a finite set of linear inequalities. The algorithm’s main advantages are its simplicity and noise tolerance. The algorithm’s main disadvantage is its
On HigherOrder Perceptron Algorithms ∗
"... A new algorithm for online learning linearthreshold functions is proposed which efficiently combines secondorder statistics about the data with the ”logarithmic behavior ” of multiplicative/dualnorm algorithms. An initial theoretical analysis is provided suggesting that our algorithm might be vi ..."
Abstract
 Add to MetaCart
be viewed as a standard Perceptron algorithm operating on a transformed sequence of examples with improved margin properties. We also report on experiments carried out on datasets from diverse domains, with the goal of comparing to known Perceptron algorithms (firstorder, secondorder, additive
A secondorder perceptron algorithm
, 2005
"... Kernelbased linearthreshold algorithms, such as support vector machines and Perceptronlike algorithms, are among the best available techniques for solving pattern classification problems. In this paper, we describe an extension of the classical Perceptron algorithm, called secondorder Perceptr ..."
Abstract

Cited by 82 (22 self)
 Add to MetaCart
Kernelbased linearthreshold algorithms, such as support vector machines and Perceptronlike algorithms, are among the best available techniques for solving pattern classification problems. In this paper, we describe an extension of the classical Perceptron algorithm, called second
The Perceptron algorithm vs. Winnow:
, 1997
"... linear vs. logarithmic mistake bounds when few input variables are relevant1 ..."
Abstract
 Add to MetaCart
linear vs. logarithmic mistake bounds when few input variables are relevant1
The Curse of Dimensionality and the Perceptron Algorithm
, 1995
"... This paper addresses the familiar problem of predicting with a linear threshold function. The instances are Ndimensional real vectors, and a threshold function is given by an Ndimensional real weight vector w and a real threshold `. The linear threshold function has the value 1 on an instance x if ..."
Abstract
 Add to MetaCart
This paper addresses the familiar problem of predicting with a linear threshold function. The instances are Ndimensional real vectors, and a threshold function is given by an Ndimensional real weight vector w and a real threshold `. The linear threshold function has the value 1 on an instance x if w \Delta x * `, and the value 0 otherwise.
On HigherOrder Perceptron Algorithms
"... Abstract A new algorithm for online learning linearthreshold functions is proposed whichefficiently combines secondorder statistics about the data with the "logarithmic ..."
Abstract
 Add to MetaCart
Abstract A new algorithm for online learning linearthreshold functions is proposed whichefficiently combines secondorder statistics about the data with the "logarithmic
Results 1  10
of
34,920