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. Platform: |
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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. Platform: |
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Description: 关于数据挖掘中SVM，支持向量机的学习课件，都是PPT，内容详实，可作为讲课与学习的参考资料。-Data mining on the SVM, support vector machine learning courseware are PPT, informative, can be used as reference for lectures and learning. Platform: |
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Author:杜以华 |
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Description: 数据挖掘与机器学习经典教材，希望对相关领域研究人员有所帮助-Principles and Theory for Data Mining and Machine Learning (Springer Series in Statistics) Platform: |
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Author:虚怀若谷 |
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Description: LIBSVM is an integrated software for support vector classification, (C-SVC, nu-SVC), regression (epsilon-SVR, nu-SVR) and distribution estimation (one-class SVM). It supports multi-class classification.
Since version 2.8, it implements an SMO-type algorithm proposed in this paper:
R.-E. Fan, P.-H. Chen, and C.-J. Lin. Working set selection using second order information for training SVM. Journal of Machine Learning Research 6, 1889-1918, 2005. You can also find a pseudo code there. (how to cite LIBSVM)
Our goal is to help users other fields to easily use SVM as a tool. LIBSVM provides a simple interface where users can easily link it with their own programs. Main features of LIBSVM include-LIBSVM is an integrated software for support vector classification, (C-SVC, nu-SVC), regression (epsilon-SVR, nu-SVR) and distribution estimation (one-class SVM). It supports multi-class classification.
Since version 2.8, it implements an SMO-type algorithm proposed in this paper:
R.-E. Fan, P.-H. Chen, and C.-J. Lin. Working set selection using second order information for training SVM. Journal of Machine Learning Research 6, 1889-1918, 2005. You can also find a pseudo code there. (how to cite LIBSVM)
Our goal is to help users other fields to easily use SVM as a tool. LIBSVM provides a simple interface where users can easily link it with their own programs. Main features of LIBSVM include Platform: |
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Author:carl2380 |
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Description: libsvm-3.22.rar LIBSVM is an integrated software for support vector classification, (C-SVC, nu-SVC), regression (epsilon-SVR, nu-SVR) and distribution estimation (one-class SVM). It supports multi-class classification.
Since version 2.8, it implements an SMO-type algorithm proposed in this paper:
R.-E. Fan, P.-H. Chen, and C.-J. Lin. Working set selection using second order information for training SVM. Journal of Machine Learning Research 6, 1889-1918, 2005. You can also find a pseudo code there. (how to cite LIBSVM)
Our goal is to help users other fields to easily use SVM as a tool. LIBSVM provides a simple interface where users can easily link it with their own programs. Main features of LIBSVM include-libsvm-3.22.rar LIBSVM is an integrated software for support vector classification, (C-SVC, nu-SVC), regression (epsilon-SVR, nu-SVR) and distribution estimation (one-class SVM). It supports multi-class classification.
Since version 2.8, it implements an SMO-type algorithm proposed in this paper:
R.-E. Fan, P.-H. Chen, and C.-J. Lin. Working set selection using second order information for training SVM. Journal of Machine Learning Research 6, 1889-1918, 2005. You can also find a pseudo code there. (how to cite LIBSVM)
Our goal is to help users other fields to easily use SVM as a tool. LIBSVM provides a simple interface where users can easily link it with their own programs. Main features of LIBSVM include Platform: |
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Author:carl2380 |
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Description: 本文总结了J.P.摩根最新的280 页研究报告中的13亮点，极为详尽地梳理、预测了金融从业者未来都需要具备相关机器学习以及数据分析的能力，分析了金融行业的现状与未来，对于金融从业者以及想从事金融行业者具有重要的借鉴意义。(This paper summarizes the highlights of JP Morgan's latest 280-page study, which is a very good way to predict the future of financial practitioners who need relevant machine learning and data analysis capabilities. Analyze the status and future of the financial industry. Practitioners and those who want to engage in the financial industry have an important reference.) Platform: |
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Author:gouchen
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Description: 为了提升配网供电可靠性的预测精度!提出了基于主成分分析和粒子群优化极限学习机的配网供电可靠
性预测模型$ 从多方面分析影响供电可靠性的指标!利用主成分分析得到综合变量!实现对数据的降维$ 在此基
础上!构建人工神经网络并利用粒子群算法优化极限学习机的输入权值和阈值!完成对训练供电可靠性预测模型
的训练$ 以某大型电网的 ?L 个供电局样本 !% 种影响供电可靠性因素为例进行仿真分析!并将 E S R C E FQ C 4 G D算
法与 ! 种回归拟合算法对比!验证了该方法的有效性(It is clear that the learning speed of feedforward neural networks is in general far slower than required and it has been a major bottleneck in their applications for past decades. Two key reasons behind may be: (1) the slow gradient-based learning algorithms are extensively used to train neural networks, and (2) all the parameters of the networks are tuned iteratively by using such learning algorithms. Unlike these conventional implementations, this paper proposes a new learning algorithm called extreme learning machine (ELM) for single-hidden layer feedforward neural networks (SLFNs) which randomly chooses hidden nodes and analytically determines the output weights of SLFNs. In theory, this algorithm tends to provide good generalization performance at extremely fast learning speed. The experimental results based on a few artificial and real benchmark function approximation and classification problems including very large complex applications show that the new algorithm can p) Platform: |
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