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Abstract
We present a component-based, trainable system for detecting
frontal and near-frontal views of faces in still gray
images. The system consists of a two-level hierarchy of Support
Vector Machine (SVM) classifiers. On the first level,
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这片论文描述了动态物体的特征跟踪，用到了15个框架。拥有很强的适应性和跟踪能力。作为人脸识别，模式识别，动态跟踪的开发人员，有很好的参考价值。用c++编写，如果用OpenCV更好-This paper describes a visual object detection framework that is capable of processing
images extremely rapidly while achieving high detection rates. There ar
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介绍模式识别的基本概念，详述了贝叶斯，参数估计，线性分类器，神经网络，随机方法，无监督学习与聚类等-Introduce the basic concepts of pattern recognition, Bayesian detailed, parameter estimation, linear classifiers, neural networks, stochastic methods, unsupervised learning and clustering, etc.
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Many state-of-the-art approaches for object recognition
reduce the problem to a 0-1 classifi cation task. Such re-
ductions allow one to leverage sophisticated classifi ers for
learning. These models are typically trained independentl
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里面包含了手写数字识别代码，有PCA特征提取，FSVM分类器识别，是很好的学习资源-Which contains a handwritten digital identification code, PCA feature extraction, FSVM classifiers recognition, is a good learning resource
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该代码采用matlab和C混合编写，是采用随机梯度的方法训练线性分类器，它可用于数据、图像等的多类分类情况。参考论文：Good Practice in Large-Scale Learning for Image Classification-This a Stochastic Gradient Descent algorithm used to train linear multiclass classifiers. It is biased towards large classificat
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