监督学习

一种基于有监督学习原理的Web服务选择方法

A Method of Choosing Web Services based on Supervised Learning Principle

给出了ISA视角子空间分析公式和有效的多视角子空间有监督学习算法。

A series of formulation and a supervised learning algorithm of multi-views subspace analysis were investigated and obtained .

一种基于半监督学习的多模态Web查询精化方法

Multi-Modal Web Search Query Refinement Based on Semi-Supervised Learning

融入半监督学习的思想，提出了PCSEM算法。

Absorbing the main thoughts of semi-supervised learning , PC_SEM is presented .

一种改进的无监督学习SVM及其在故障识别中的应用

Decision improving of unsupervised SVM for fault identification

针对用于BP网络训练的半监督学习算法，提出了一种新的实用的训练结束判据，并将其应用于电力系统暂态稳定分类中。

A useful ending-criterion is proposed for semi-supervised BP algorithm , and applied it to transient stability classification of power systems .

面对战争系统中复杂动态的环境，传统的监督学习方法不能很好满足智能Agent实时学习的要求。

Because of the complex and dynamic environment , the traditional supervise learning theory can not be fulfil for the Agent real time learning very well .

该方案使用的算法就是基于FCM的半监督学习算法，同时可以进行特征选择。

It uses semi-supervised learning algorithm based on FCM , and for feature selection .

设计一种基于BP神经网络的监督学习控制器（SNC）。

A type of supervisory and learning controller based on BP neural network is designed .

使用一个半监督学习算法，ANN可产生一个能够指示相对稳定度的连续分布的暂态稳定指标。

The ANN can derive a continuous-spread stability index to indicate the relative stability degree by means of a semi-supervised learning algorithm .

基于有监督学习的多视角人脸模型子空间学习算法，能够实现比无监督ISA学习算法得到更高的姿态估计精度。

It can gain more accuracy than the ( unsupervised ) learning algorithm in pose information estimation .

半监督学习（semi-supervisedlearning）是利用未标记样本进行学习的主流技术，是目前机器学习中非常活跃的研究方向之一。

Semi-supervised learning is the primary method of making use of unlabeled samples for learning , and is a very active research field in machine learning .

在线性最优励磁控制的基础上，利用3层BP神经网络对柴油发电机的控制过程进行监督学习。

On the basis of linear optimal exciting control , a three layered BP neural network is used for supervising and learning the controlling process of brushless diesel generator .

本文主要讨论在非监督学习中，用EM方法求解有限混合分布（已知类别数）未知参数的问题。

We discuss how the EM ( Expectation Maximization ) algorithm can be used for parameter estimation of finite mixture distributions in unsupervised learning .

词类标注的消歧规则的无监督学习〉。《超大规模语料专题讨论会》，1995年。（PDF）

" Unsupervised Learning of Disambiguation Rules for Part of Speech Tagging . " Workshop on Very Large Corpora , p.1995 . ( PDF )

提出了一种连续搅拌反应釜（CSTR）的混合监督学习控制方法。

A new hybrid supervised learning control scheme is presented for continuous stirred tank reactor ( CSTR ) systems .

实验结果表明，不对称的半监督学习策略及随机子空间理论可有效地改进SVM反馈性能，且所提出算法明显优于同类SVM反馈方法。

The experimental evaluations show that the proposed asymmetric semi-supervised learning methodology in conjunction with random subspace theory is effective to improve CBIR performance and achieves better performance than some existing approaches .

本论文提出了一种有效地结合SVM和LPP的半监督学习算法&LPP半监督算法。

In this thesis , we introduce a new semi-supervised learning algorithm : Semi-supervised LPP algorithm , which efficiently combines the characters of both SVM and LPP .

RBF神经网络作为基础网络，采用高斯函数实现输入层到隐层的非线性映射，输出层则采用有监督学习算法训练网络的权值，从而实现输入层到输出层的非线性映射。

RBF network , as a basic network , performed the nonlinear mapping from input nodes to hidden nodes using Gauss function and linear mapping from hidden nodes to output nodes using supervised learning algorithm .

根据地下水位与其影响因素之间存在的映射关系，建立了一种改进的RBF神经网络模型，并分别通过减聚类和监督学习算法对网络参数和权值进行训练。

Based on the relationship between the groundwater level and its main influential factors , by using subtractive clustering and supervised learning algorithm for training , an improved learning algorithm for RBF neural networks is presented .

为了对在线学习文档进行分类，本文根据自适应谐振理论给出了一个半监督学习模糊ART模型（SLFART）及其算法。

For learning document classification on line , the paper gives the semi-supervised learning fuzzy ART model ( SLFART ) based on adaptive resonance theory and the models algorithm .

首先由改进的FCM算法确定神经网络结构；然后利用监督学习对网络参数进一步优化，并对输出权值调整。

In the process of training , the structure of neural network is determined by using improved FCM algorithm , then a supervised learning scheme is used to tune the parameters of neural network fine .

证明了一类Kohonen自监督学习子空间方法的收敛性；

The transformed matrices of learning subspaces are proved to converge to the estimation of pattern autocorrelation matrix , thus the convergence of Kohonen 's self-supervised LSMs is proved .

目前，ICA已经成为盲源分离的主流方法。BSS与ICA都属于一类无监督学习算法，他们的算法理论与实际应用涉及到优化数学和神经科学等多个领域。

The methods of BSS and ICA are belong to a class of unsupervised learning algorithm , its algorithm theory and practical application are related the optimization of a number of areas of mathematics and neuroscience .

对三种半监督学习算法Self-Training、Co-Training和Tri-Training结合不同的基分类器通过实验来进行性能评估。

Based on different base classifier algorithms , experiments evaluate performance on three semi-supervised learning algorithms including Self-Training , Co-Training and Tri-Training .

该算法基于贝叶斯决策理论，通过概率密度函数进行分布估计，对两类别半监督学习问题进行判定。（3）给出了一个基于FCM的半监督学习算法。

The algorithm is based on Bayesian decision machinery , with estimation of the structure from probability density function , for two-class problems . ( 3 ) The paper gives out a semi-supervised learning algorithm based on FCM .

本文在深入研究半监督学习和传统的机器学习理论及目前入侵检测系统所面临问题的基础上，选择SVM方法作为基础分类算法，提出了一种新的处理海量未标记数据的半监督算法。

SVM has been recently used in many applications . We deeply research the Semi-supervised Learning , machine learning and the problems of current intrusion detection system . The research in this paper provides new methods based on SVM for solving mass unlabeled data .

同时，RCAS算法结合了机器学习中半监督学习和相关反馈算法的优点，对有关类和无关类中未标记样本采用不同的方式加以利用。

Meanwhile , RCAS employs semi-supervised learning and relevance feedback to deal with the unlabeled samples in the related class and unrelated class .

首先根据输入输出模式确定网络结构，然后在有监督学习和误差最小化的前提下，利用变量梯度法和LMS算法确定网络参数。

Firstly the network architecture is decided according to the input-output pattern , then network parameters can be updated according to the hybrid algorithm that integrates gradient decent algorithm and LMS algorithm on the basis of supervised learning and error minimization .

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。

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 .