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CONSTANT COUNT-A SOLUTION TO PROBLEMS OF QUADRAT SIZE

by G. T. Jane
"... presented in full elsewhere in this volume). For the complete programme of papers given at this conference please refer to the Annual Report at the back of the Journal. ..."
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presented in full elsewhere in this volume). For the complete programme of papers given at this conference please refer to the Annual Report at the back of the Journal.

On the optimality of the simple Bayesian classifier under zero-one loss

by Pedro Domingos, Michael Pazzani - MACHINE LEARNING , 1997
"... The simple Bayesian classifier is known to be optimal when attributes are independent given the class, but the question of whether other sufficient conditions for its optimality exist has so far not been explored. Empirical results showing that it performs surprisingly well in many domains containin ..."
Abstract - Cited by 818 (27 self) - Add to MetaCart
containing clear attribute dependences suggest that the answer to this question may be positive. This article shows that, although the Bayesian classifier’s probability estimates are only optimal under quadratic loss if the independence assumption holds, the classifier itself can be optimal under zero

On-Line Construction of Suffix Trees

by Esko Ukkonen , 1995
"... An on-line algorithm is presented for constructing the suffix tree for a given string in time linear in the length of the string. The new algorithm has the desirable property of processing the string symbol by symbol from left to right. It has always the suffix tree for the scanned part of the strin ..."
Abstract - Cited by 437 (2 self) - Add to MetaCart
of the string ready. The method is developed as a linear-time version of a very simple algorithm for (quadratic size) suffix tries. Regardless of its quadratic worst-case this latter algorithm can be a good practical method when the string is not too long. Another variation of this method is shown to give in a

Efficient belief propagation for early vision

by Pedro F. Felzenszwalb, Daniel P. Huttenlocher - In CVPR , 2004
"... Markov random field models provide a robust and unified framework for early vision problems such as stereo, optical flow and image restoration. Inference algorithms based on graph cuts and belief propagation yield accurate results, but despite recent advances are often still too slow for practical u ..."
Abstract - Cited by 515 (8 self) - Add to MetaCart
use. In this paper we present new algorithmic techniques that substantially improve the running time of the belief propagation approach. One of our techniques reduces the complexity of the inference algorithm to be linear rather than quadratic in the number of possible labels for each pixel, which

On the algorithmic implementation of multi-class kernel-based vector machines

by Koby Crammer, Yoram Singer, Nello Cristianini, John Shawe-taylor, Bob Williamson - Journal of Machine Learning Research
"... In this paper we describe the algorithmic implementation of multiclass kernel-based vector machines. Our starting point is a generalized notion of the margin to multiclass problems. Using this notion we cast multiclass categorization problems as a constrained optimization problem with a quadratic ob ..."
Abstract - Cited by 559 (13 self) - Add to MetaCart
In this paper we describe the algorithmic implementation of multiclass kernel-based vector machines. Our starting point is a generalized notion of the margin to multiclass problems. Using this notion we cast multiclass categorization problems as a constrained optimization problem with a quadratic

Sequential minimal optimization: A fast algorithm for training support vector machines

by John C. Platt - Advances in Kernel Methods-Support Vector Learning , 1999
"... This paper proposes a new algorithm for training support vector machines: Sequential Minimal Optimization, or SMO. Training a support vector machine requires the solution of a very large quadratic programming (QP) optimization problem. SMO breaks this large QP problem into a series of smallest possi ..."
Abstract - Cited by 461 (3 self) - Add to MetaCart
is avoided, SMO scales somewhere between linear and quadratic in the training set size for various test problems, while the standard chunking SVM algorithm scales somewhere between linear and cubic in the training set size. SMO’s computation time is dominated by SVM evaluation, hence SMO is fastest

Very Large-Scale Neighborhood Search for the Quadratic Assignment Problem

by Ravindra K. Ahuja, Krishna C. Jha, James B. Orlin, Dushyant Sharma - DISCRETE APPLIED MATHEMATICS , 2002
"... The Quadratic Assignment Problem (QAP) consists of assigning n facilities to n locations so as to minimize the total weighted cost of interactions between facilities. The QAP arises in many diverse settings, is known to be NP-hard, and can be solved to optimality only for fairly small size instances ..."
Abstract - Cited by 150 (13 self) - Add to MetaCart
The Quadratic Assignment Problem (QAP) consists of assigning n facilities to n locations so as to minimize the total weighted cost of interactions between facilities. The QAP arises in many diverse settings, is known to be NP-hard, and can be solved to optimality only for fairly small size

Evaluating 2-dnf formulas on ciphertexts

by Dan Boneh, Eu-jin Goh, Kobbi Nissim - In proceedings of TCC ’05, LNCS series , 2005
"... Abstract. Let ψ be a 2-DNF formula on boolean variables x1,..., xn ∈ {0, 1}. We present a homomorphic public key encryption scheme that allows the public evaluation of ψ given an encryption of the variables x1,..., xn. In other words, given the encryption of the bits x1,..., xn, anyone can create th ..."
Abstract - Cited by 231 (7 self) - Add to MetaCart
the encryption of ψ(x1,..., xn). More generally, we can evaluate quadratic multi-variate polynomials on ciphertexts provided the resulting value falls within a small set. We present a number of applications of the system: 1. In a database of size n, the total communication in the basic step of the Kushilevitz

Efficient Search for Approximate Nearest Neighbor in High Dimensional Spaces

by Eyal Kushilevitz, Rafail Ostrovsky, Yuval Rabani , 1998
"... We address the problem of designing data structures that allow efficient search for approximate nearest neighbors. More specifically, given a database consisting of a set of vectors in some high dimensional Euclidean space, we want to construct a space-efficient data structure that would allow us to ..."
Abstract - Cited by 215 (9 self) - Add to MetaCart
is polynomial in the size of the database, and search algorithms that run in time nearly linear or nearly quadratic in the dimension (depending on the case; the extra factors are polylogarithmic in the size of the database). Computer Science Department, Technion --- IIT, Haifa 32000, Israel. Email: eyalk

Quadratic Bloat in Genetic Programming

by William B. Langdon - Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2000), pages 451–458, Las Vegas , 2000
"... In earlier work we predicted program size would grow in the limit at a quadratic rate and up to fifty generations we measured bloat O(generations 1:2\Gamma1:5 ). On two simple benchmarks we test the prediction of bloat O(generations 2:0 ) up to generation 600. In continuous problems the li ..."
Abstract - Cited by 42 (3 self) - Add to MetaCart
In earlier work we predicted program size would grow in the limit at a quadratic rate and up to fifty generations we measured bloat O(generations 1:2\Gamma1:5 ). On two simple benchmarks we test the prediction of bloat O(generations 2:0 ) up to generation 600. In continuous problems
Results 1 - 10 of 2,239