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LVQ学习矢量化算法
- LVQ学习矢量化算法源程序 This directory contains code implementing the Learning vector quantization network. Source code may be found in LVQ.CPP. Sample training data is found in LVQ1.PAT. Sample test data is found in LVQTEST1.TST and LVQTEST2.TST. The
co-training
- 半监督学习co-training 回归算法的java代码实现。-COREG is a co-training style semi-supervised regression algorithm, which employs two kNN regressors using different distance metrics to select the most confidently labeled unlabeled examples for each other.
a
- Matlab矢量量化图像压缩程序 矢量量化VQ，是一个常见的压缩技术。Linde, Buzo, and Gray 提出了基于 训练序列的Vector Quantization (VQ)算法。-Matlab vector quantization image compression procedures for vector quantization VQ, is a common compression technology. Linde, Buzo, and Gray proposed
QuantitativeAnalysisofGasMixtureUsinganArtificialN
- 优选了分析CO、H2和CH4混合气体的传感器阵列,构造了传感器信号预处理和神经网络 训练算法,从而建立了用于混合气体定量分析的人工嗅觉系统。实验结果证明,系统能够以较高的 精度分辨出3种气体的浓度。 -Optimization of the analysis of CO, H2 and CH4 mixed gas sensor array, constructs a sensor signal pre-processing and neural network training al
developmentofchinesetelecommunication
- 这是深圳市华为技术有限公司培训内部员工的内部资料，主要由朱高峰院士，陈俊吴佑寿院士，周炳琨院士，简水生院士，童志鹏院士等等介绍中国电信技术行业的发展-This is Shenzhen Huawei Technologies Co., Ltd. internal staff training internal information, the main peak by the Chinese Academy of Sciences, Academician Wu Youshou Chen, Zho
2009082504
- 北京微芯力科技有限公司ARM内部培训教程，资源共享-Microchip Technology Co., Ltd. Beijing Power ARM-house training tutorials, the sharing of resources! ! !
Company_Culture_Training
- 汇才人力技术有限公司企业传播部经理，现役专业教练。曾作为指导员参与深圳海洋王投资发展有限公司、广州物资几天等企业的各类训练。黄先生从前主要进行现代诗方面的创作，并在多家报刊上发表作品。1999年接触“教练”后，写作了大量管理类作品，发表于《经理人》、《经济月刊》、《中国青年报》、《南方人才》等媒体，并为《成功》杂志和《羊城晚报》撰写专栏文章。《教练的智慧》是他将文学创作功底与教练文化体验相结合的第一本作品。-Department of Human Technology Co., Ltd. Cor
IDL-trainning-by-company
- 北京信息时空科技有限公司IDL培训教程当时培训idl开发时候的培训文档和源码，都放一块了-Beijing Information Technology Co., Ltd. IDL space-time training idl was developed training curricula for training when the documentation and source code, have put one of the
Video_semantic
- 本文提出了多个基于半监督学习的自动视频标注方法。通过对几种常见的半监督学习方法，如自训练、互训练以及Co一EM等方法的分析，针对它们(主要是自训练和互训练方法)在视频标注应用中的局限，在提高分类的准确性和模型更新等方面做了深入研究，提出了相应的改进措施。 -This paper presents a number of semi-supervised learning-based automatic video annotation methods. Through several comm
SemiSupervisedLearning
- 第一本全面的半监督学习参考书，里面涵盖了半监督方面的各种算法，应用，以及理论证明-This is a comprehensive reference of semi-surpervised learning, including the algrithms, application, and the proof in theory.
2009Based_on_Co-Training
- Co-Training的协同目标跟踪内容，感兴趣的看看吧-Co-Training for collaborative target tracking content, of interest to see it
CoTrade
- COTRADE算法通过MATLAB编写，通过数据和修改相关技术，实现了传统的协同算法的加强-The package includes the MATLAB code of COTRADE, which is designed for enhancing traditional co-training algorithm by incorporating data editing techniques.
java-Training-beijing
- 北京 圣思园教育科技有限公司第一期面授 培训大纲 -Beijing SAN think the education science and technology Co., LTD, the first period face to award Training outline
libsvm-3.11
- co-training协同训练算法研究 svm工具箱-co-training self-training
pixeltrack_v0.1
- a novel algorithm for fast tracking of generic objects in videos. The algorithm uses two components: a detector that makes use of the generalised Hough transform with pixel-based descr iptors, and a probabilistic segmentation method based on global m
MT5000-training-senior
- 触摸屏作为一种人机界面，是操作人员和机械设备之间做双向沟通的桥梁，用户可以自由组合文字、按钮、图形、数字等来处理或者监控管理及应付随时可能变化信息的多功能显示屏幕。 随着机械设备的飞速发展，以往的操作界面需由熟练的操作员才能操作，而且操作困难，无法提高工作效率。但是使用人机界面能够明确指示并告知操作员机器设备目前的状况，使操作变的简单生动，并且可以减少操作上的失误，即使新手也可以很轻松的操作整个机器设备。使用人机界面还可以使机器的配线标准化、简单化，同时也能减少PLC控制器所需的I/O点数，
7----Co-training-A-SEMI-SUPERVISED-ENSEMBLE-LEARN
- 7 - Co-training A SEMI-SUPERVISED ENSEMBLE LEARNING ALGORITHM-6-7 - Co-training A SEMI-SUPERVISED ENSEMBLE LEARNING ALGORITHM-6
cotraining-master
- 用java实现了co-training，用于少量标注数据来获取大量的标注数据，也在于解决数据不均衡问题(This is an algorithm for aspect-based sentiment analysis using co-training, a semi-supervised machine learning algorithm that partitions the machine learning features into two sufficient and uncorre
CoTrade
- The package includes the Matlab code of COINS, which is designed for learning from multi-label data under the inductive semi-supervised setting by adapting the co-training techniques. Source code as well as running demo are included in the package.
co-training
- matlab实现co-training算法，数据集mutiple+Features(Matlab implements co-training algorithm, data set mutiple+Features.)