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Description: Learning Kernel Classifiers: Theory and Algorithms, Introduction This chapter introduces the general problem of machine learning and how it relates to statistical inference. 1.1 The Learning Problem and (Statistical) Inference It was only a few years after the introduction of the first computer that one of man’s greatest dreams seemed to be realizable—artificial intelligence. Bearing in mind that in the early days the most powerful computers had much less computational power than a cell phone today, it comes as no surprise that much theoretical research on the potential of machines’ capabilities to learn took place at this time. This becomes a computational problem as soon as the dataset gets larger than a few hundred examples.-Learning Kernel Classifiers : Theory and Algorithms. Introduction This chapter introduces the gene the acidic problem of machine learning and how it relat es to statistical inference. 1.1 The Learning P roblem and (Statistical) It was only inference a few years after the introduction of the first c omputer that one of man's greatest dreams seeme d to be realizable-artificial intelligence. B earing in mind that in the early days the most pow erful computers had much less computational po wer than a cell phone today, it comes as no surprise that much theoretical're search on the potential of machines' capabilit ies to learn took place at this time. This become 's a computational problem as soon as the dataset gets larger than a few hundred examples.
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Size: 2537081 |
Author: google2000 |
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Description: 向量空间模型分类器
A vector-space model classifiers package
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Size: 5328 |
Author: sisn |
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Description: 数据挖掘classifiers算法，用JAVA实现的分类算法。
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Size: 372002 |
Author: jovi |
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Description: 模式识别中人连识别眼镜定位，用的是matlab支持向量机开发的-human pattern recognition to identify even glasses positioning, using the Matlab SVM Development
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Size: 451584 |
Author: 网小强 |
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Description: 基于多分类器组合的笔迹验证 －－文章-Based on the composition of the classifier based on the handwriting test multiple classifiers combination of handwriting authentication-- article
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Size: 216064 |
Author: Yuan |
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Description: Rapid Object Detection With A Cascade of Boosted Classifiers Based on Haar-like Features-Rapid Object Detection With A Cascade of Bo osted Classifiers Based on Haar- like Features
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Size: 257024 |
Author: 黄笑 |
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Description: Learning Kernel Classifiers: Theory and Algorithms, Introduction This chapter introduces the general problem of machine learning and how it relates to statistical inference. 1.1 The Learning Problem and (Statistical) Inference It was only a few years after the introduction of the first computer that one of man’s greatest dreams seemed to be realizable—artificial intelligence. Bearing in mind that in the early days the most powerful computers had much less computational power than a cell phone today, it comes as no surprise that much theoretical research on the potential of machines’ capabilities to learn took place at this time. This becomes a computational problem as soon as the dataset gets larger than a few hundred examples.-Learning Kernel Classifiers : Theory and Algorithms. Introduction This chapter introduces the gene the acidic problem of machine learning and how it relat es to statistical inference. 1.1 The Learning P roblem and (Statistical) It was only inference a few years after the introduction of the first c omputer that one of man's greatest dreams seeme d to be realizable-artificial intelligence. B earing in mind that in the early days the most pow erful computers had much less computational po wer than a cell phone today, it comes as no surprise that much theoretical're search on the potential of machines' capabilit ies to learn took place at this time. This become 's a computational problem as soon as the dataset gets larger than a few hundred examples.
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Description: 神经网络感知器做的分类器的源码，适合神经网络初学者-Perceptron neural network classifiers make the source code, suitable for beginners neural network
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Size: 1924096 |
Author: zhanglei |
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Description: This sourse is for face detection in the image and it is writed by VC. Here we use the adaboost for the main mathed
In the mathed it use five features classifiers!
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Size: 5948416 |
Author: Andy |
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Description: Boosting is a meta-learning approach that aims at combining an ensemble of weak classifiers to form a strong classifier. Adaptive Boosting (Adaboost) implements this idea as a greedy search for a linear combination of classifiers by overweighting the examples that are misclassified by each classifier. icsiboost implements Adaboost over stumps (one-level decision trees) on discrete and continuous attributes (words and real values). See http://en.wikipedia.org/wiki/AdaBoost and the papers by Y. Freund and R. Schapire for more details [1]. This approach is one of most efficient and simple to combine continuous and nominal values. Our implementation is aimed at allowing training from millions of examples by hundreds of features in a reasonable time/memory.
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Size: 116736 |
Author: njustyw |
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Description: 向量空间模型分类器
A vector-space model classifiers package-Vector space model classifier A vector-space model classifiers package
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Size: 5120 |
Author: sisn |
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Description: 数据挖掘classifiers算法，用JAVA实现的分类算法。-Data mining classifiers algorithm, using JAVA realize the classification algorithm.
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Size: 371712 |
Author: jovi |
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Description: 用VC++实现图像的分类识别，模板匹配分类器，Bayes分类器，线性函数分类法，非线性分类法，神经网络分类器-With VC++ Achieve image classification and recognition, template matching classifier, Bayes classifier, a linear function of classification, non-linear classification, neural network classifiers
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Size: 5160960 |
Author: gaomiao |
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Description: 从因子分析的角度出发解决基因表达谱分析问题。为解决独立成分分析方法在求解过程中的不稳定性，提出一种基于选择性独立成分分析的DNA微阵列数据集成分类器。首先对基因表达水平的重构误差进行分析，选择部分重构误差较小的独立成分进行样本重构，然后基于重构后的样本同时训练多个支持向量机基分类器，最后选择部分分类正确率较高的基分类器进行最大投票以得到最终结果。在3个常用测试集上验证了本文设计方法的有效性。-This paper tries to deal with gene expression problem in view of factor analysis. In order to overcome the instability problem caused by performing independent component analysis, a DNA microarray data ensemble classifier based on selective independent component analysis is proposed. The reconstruction error of each gene is analyzed firstly and a part of independent components which contribute relatively small reconstruction errors are selected to reconstruct new samples. After that, several support vector machine base classifiers are trained simultaneously. Finally, the best base classifiers with high correct rates are selected to participate in the ensemble, using the majority voting method. Results on three publicly available microarray datasets show the feasibility and validity of the method proposed in this paper.
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Author: cumtgyy |
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Description: 流分类算法中的一种，Scalable Packet Classification 非常有参考价值-Packet classification is important for applications such as
firewalls, intrusion detection, and differentiated services. Existing
algorithms for packet classification reported in the literature
scale poorly in either time or space as filter databases
grow in size. Hardware solutions such as TCAMs do not
scale to large classifiers. However, even for large classifiers
(say 100,000 rules), any packet is likely to match a few (say
10) rules. Our paper seeks to exploit this observation to
produce a scalable packet classification scheme called Aggregated
Bit Vector (ABV). Our paper takes the bit vector
search algorithm (BV) described in [11] (which takes linear
time) and adds two new ideas, recursive aggregation of
bit maps and filter rearrangement, to create ABV (which
can take logarithmic time for many databases). We show
that ABV outperforms BV by an order of magnitude using
simulations on both industrial firewall databases and synthetically
generated databases.
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Size: 193536 |
Author: Reguse |
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Description: The matlab code implements the ensemble of decision tree classifiers proposed in: "L. Nanni and A. Lumini, Input Decimated Ensemble based on Neighborhood Preserving Embedding for spectrogram classification, Expert Systems With Applications doi:10.1016/j.eswa.2009.02.072 "
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Size: 1024 |
Author: loris nanni |
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Description: 利用matlab软件对模式识别进行了很好的仿真，利用贝叶斯分类器对男女生进行了很好的分类。-Pattern recognition using matlab software had a very good simulation, the use of Bayesian Classifiers for boys and girls had a very good classification.
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Size: 47104 |
Author: 倪继峰 |
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Description: This package contains Matlab m-files for learning finite Gaussian mixtures from sample data and performing data classification with Mahalanobis distance or Bayesian classifiers. Each class in training set is learned individually with one of the three variations of the Expectation Maximization algorithm: the basic EM algorithm with covariance fixing, the Figueiredo-Jain clustering algorithm and the greedy EM algorithm.
The basic EM and FJ algorithms can handle complex valued data directly, the greedy EM algorithm cannot.
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Size: 20480 |
Author: 李 |
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Description: Probabilistic Fuzzy Classifiers
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Size: 163840 |
Author: hosein |
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Description: 我们需要成百上千的分类器来解决现实世界的分类吗
我们评估179分类17种分类器（判别分析，贝叶斯，神经网络，支持向量机，决策树，基于规则的分类器，升压、装袋、堆放、随机森林和其他合奏，广义线性模型，线性，偏最小二乘法和主成分回归，logistic回归、多项式回归、多元自适应回归样条等方法），实现在WEKA，R（有或没有插入包），C和Matlab，包括所有目前可用的相关分类。(Do-we-Need-Hundreds-of-Classifiers-to-Solve-Real-World-ClassificationProblems)
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Size: 537600 |
Author: 飞飞花儿
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