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Automatic Detection of Errors on Textures Using Invariant Grey Scale Features and Polynomial Classifiers
 Texture Analysis in Machine Vision, volume 40 of Machine Perception and Artificial Intelligence
, 1999
"... In this paper we propose two methods for the automatic detection of errors on nonstochastic textures. Both methods are based on invariant grey scale features and may be distinguished by their global or local approach, respectively. Classication of the nonlinear invariant features is done by a poly ..."
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Cited by 5 (4 self)
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polynomial classier of third degree. Our test application for the evaluation of the invariant features is the error detection on textile surfaces. Experimental results based on the image database TILDA are presented and discussed in this contribution.
EURASIP Journal on Applied Signal Processing 2005:13, 2136–2145 c ○ 2005 Hindawi Publishing Corporation Recognition of Arabic Sign Language Alphabet Using Polynomial Classifiers
, 2004
"... Building an accurate automatic sign language recognition system is of great importance in facilitating efficient communication with deaf people. In this paper, we propose the use of polynomial classifiers as a classification engine for the recognition of Arabic sign language (ArSL) alphabet. Polynom ..."
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Building an accurate automatic sign language recognition system is of great importance in facilitating efficient communication with deaf people. In this paper, we propose the use of polynomial classifiers as a classification engine for the recognition of Arabic sign language (ArSL) alphabet
Comparing Support Vector Machines with Gaussian Kernels to Radial Basis Function Classifiers
 IEEE TRANSACTIONS ON SIGNAL PROCESSING
, 1997
"... The Support Vector (SV) machine is a novel type of learning machine, based on statistical learning theory, which contains polynomial classifiers, neural networks, and radial basis function (RBF) networks as special cases. In the RBF case, the SV algorithm automatically determines centers, weights an ..."
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Cited by 183 (13 self)
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The Support Vector (SV) machine is a novel type of learning machine, based on statistical learning theory, which contains polynomial classifiers, neural networks, and radial basis function (RBF) networks as special cases. In the RBF case, the SV algorithm automatically determines centers, weights
Classifying polynomials and identity testing
, 2009
"... email: One of the fundamental problems of computational algebra is to classify polynomials according to the hardness of computing them. Recently, this problem has been related to another important problem: Polynomial identity testing. Informally, the problem is to decide if a certain succinct repre ..."
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Cited by 8 (1 self)
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email: One of the fundamental problems of computational algebra is to classify polynomials according to the hardness of computing them. Recently, this problem has been related to another important problem: Polynomial identity testing. Informally, the problem is to decide if a certain succinct
The Quadratic Eigenvalue Problem
, 2001
"... . We survey the quadratic eigenvalue problem, treating its many applications, its mathematical properties, and a variety of numerical solution techniques. Emphasis is given to exploiting both the structure of the matrices in the problem (dense, sparse, real, complex, Hermitian, skewHermitian) and t ..."
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Cited by 260 (21 self)
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Hermitian) and the spectral properties of the problem. We classify numerical methods and catalogue available software. Key words. quadratic eigenvalue problem, eigenvalue, eigenvector, matrix, matrix polynomial, secondorder differential equation, vibration, Millennium footbridge, overdamped system, gyroscopic system
Classifying Real Polynomial Pencils
"... Let be the space of all homogeneous polynomials of degree n in two variables with real coecients. The standard discriminant Dn+1 is Whitney strati ed according to the number and the multiplicities of multiple real zeros. A real polynomial pencil, that is, a line L is called generic if ..."
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Cited by 4 (1 self)
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Let be the space of all homogeneous polynomials of degree n in two variables with real coecients. The standard discriminant Dn+1 is Whitney strati ed according to the number and the multiplicities of multiple real zeros. A real polynomial pencil, that is, a line L is called generic
Simplified Polynomial Network Classifier for Handwritten Character Recognition
"... Classspecific feature polynomial classifier (CFPC), a variant of a polynomial classifier (PC), yields high classification accuracy especially in high dimensional feature spaces. However, the computational cost for classification in such a high dimensional space is rather expensive. To overcome this ..."
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Classspecific feature polynomial classifier (CFPC), a variant of a polynomial classifier (PC), yields high classification accuracy especially in high dimensional feature spaces. However, the computational cost for classification in such a high dimensional space is rather expensive. To overcome
Speaker Identification Using A PolynomialBased Classifier
 in International Symposium on Signal Processing and its Applications
, 1999
"... A new set of techniques for using polynomialbased classifiers for speaker identification is examined. This set of techniques makes application of polynomial classifiers practical for speaker identification by enabling discriminative training for large data sets. The training technique is shown to b ..."
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Cited by 3 (2 self)
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A new set of techniques for using polynomialbased classifiers for speaker identification is examined. This set of techniques makes application of polynomial classifiers practical for speaker identification by enabling discriminative training for large data sets. The training technique is shown
Classifying Polynomials Up To Shape
"... this paper can serve as a gentle introduction to them. ..."
Polynomials
, 2006
"... Time series are unstructured data; they are difficult to monitor, summarize and predict. Weather forecasts, stock market prices, medical data (ECG, EEG) are examples of nonstationary time series we wish to clean, classify and index. Segmentation organizes time series into few intervals having unifo ..."
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Time series are unstructured data; they are difficult to monitor, summarize and predict. Weather forecasts, stock market prices, medical data (ECG, EEG) are examples of nonstationary time series we wish to clean, classify and index. Segmentation organizes time series into few intervals having
Results 11  20
of
1,049