监督学习

• 9 blum a , kalai a . a note on learning from multiple - instance examples . machine learning , 1998 , 30 : 23 - 29 . 10 maron o , lozano - p erez t . a framework for multiple - instance learning
  本文分析了若干具有代表性的多示例学习算法，揭示出监督学习方法可以转化为多示例学习方法，只需将学习方法的注意焦点从对示例的区分转变到对包的区分。
• Solving multiple - instance and multiple - part learning problems with decision trees and decision rules . application to the mutagenesis problem . lecture notes in artificial intelligence 2056 , stroulia e , matwin s eds . ,
  由于本文已经揭示出多示例学习与监督学习之间具有密切联系，因此本文提出通过建立多示例集成来求解多示例学习问题。
• Experimental results illustrate that the performances of the proposed system is better than the discriminant cem ( classification expectation maximization ) algorithm , particularly when there are noise data and new patterns
  实验结果表明半监督学习自适应谐振理论系统的性能优于判别式cem算法，特别是在含有噪音和新模式数据情况下，其优势更为明显。
• Finally , most supervised learning neural networks train themselves through minimizing mean squared error . but when the neural network models trained in this way are used to do forecasting , the existence of outliers result in great imprecision
  最后，大多数监督学习神经网络是通过最小化训练集的均方差来训练网络，而野值的存在导致这种训练的神经网络模型在预测时会产生极大的不精确性。
• Secondly , aiming at the necessity of pixel - based tongue color classification system , this dissertation proposes a 2 - stage fcm algorithm . it applies the techniques of pattern classification and makes the better automation of tongue color model construction true
  其次,针对基于像素的舌颜色分类系统的需要,本文提出了一种“二次fcm算法” ,采用半监督学习的方式,较为自动化地解决了舌色苔色分布模型的建立问题。
• The supervised and unsupervised learning diagnosis methods are discussed and several improvements have been presented in the learning algorithms . the simulation results show that the proposed method can perforfti correct diagtioals iii the linear analog circuits with tolerances
  本文对模拟故障诊断的有监督学习和无监督学习方法分别进行了研究，通过对实现过程的分析，对经典的学习算法进行深入研究，并提出若干改进。
• Second , considering that ensemble learning paradigms can effectively enhance supervised learners , this paper proposes to build multi - instance ensembles to solve multi - instance problems . experiments on a real - world benchmark test show that ensemble learning paradigms can significantly enhance multi - instance learners
  由于多示例学习具有独特的性质，被认为是一种与监督学习非监督学习强化学习并列的一种新的学习框架。
• The main work includes : the research and conments about some recognition methods ; the research and comments about three kind of mathematics morphologic arithmetic ; clustering ; matlab embedded in the vb ; the difference analysis in the dynamic image and so on
  主要做的工作包括几种识别方法的研究与评述；三种数学形态学算法的实现以及各自的优劣比较；利用无监督学习进行聚类以及matlab在图像处理中的嵌入；动态图像的差分分析等。
• It overcomes the limitation in the assumption in other semi - supervised learning algorithms that probabilistic distribution of data is known , and has the strong ability of learning new patterns and correcting errors because of stability and plasticity of the adaptive resonance theory
  在该系统中取消了一般半监督学习算法中假定已知数据概率分布的条件限制，利用自适应谐振理论的稳定性和可塑性，使其具有非常强的学习新模式和纠正错误能力。