Notice something important here: in the classification problem, the goal of the learning algorithm is to minimize the error with respect to the given inputs.
请注意这里提到的一个问题：在分类问题中，学习算法的目标是把给定输入中的错误最小化。
Classification learning is often necessary when the decisions made by the algorithm will be required as input somewhere else.
如果通过算法作出的决定需要输入别的地方，这时分类学习是必要的。
Experiment shows neural network classifier that is optimized by algorithm could not only have fast learning speed but also ensure accuracy of classification.
实验结果表明：算法优化后的神经网络分类器不但学习速度快，还能保证分类精度。
The support vector machine is a learning algorithm, which has a good classification ability for limited training samples.
支撑矢量机是一种能在训练样本数很少的情况下达到很好分类推广能力的学习算法。
Ensemble learning is a research hotspot in machine learning, which can improve generalization performance of classification algorithm.
集成学习是当前机器学习的一个研究热点，它可以提高分类算法的泛化性能。
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.
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。
The algorithm combines active learning, biased classification and incremental learning to model the small sample biased learning problem in relevance feedback process.
该算法将主动式学习、有偏分类和增量学习结合起来，对相关反馈过程中的小样本有偏学习问题进行建模。
This paper presents a learning algorithm for a RBF neural network with adjustable radial basis width and discusses its application in the classification problem of share tendency patterns.
提出一个基宽度可调的RBF神经网络学习算法，并将它应用于个股走势模式的分类问题。
A learning algorithm based on a hard limiter for feedforward neural networks (NN) is presented, and is applied in solving classification problems on separable convex sets and disjoint sets.
提出了基于硬限幅功能函数的前向神经网络的分类学习算法，并将其应用于可分凸集或不交集合的分类。
BP algorithm has aptitude and auto-learning characters, so my paper choose BP neural net algorithm to set up mail classification and recognition model.
BP算法具有智能性和自学习性的特点，因此，本文提出采用BP神经网络来构造邮件分类识别器。
Support Vector Machines algorithm is a simple and effective classification method based upon statistical learning theory.
支持向量机是基于统计学习理论框架下的一种简单、有效的分类方法。
In this paper, learning algorithm for solving multi-category classification using convex upper losses is studied.
本文研究基于凸风险最小化方法的多分类贪婪算法，推广二分类的学习问题到多分类的情形。
In those lazy learning algorithms most extensively used is nearest neighbor classification (NN) algorithm.
其中消极学习型中应用最广泛的是最近邻分类算法。
A data classification algorithm on multiple manifolds is presented. The algorithm roughly divides into two steps: learning process and testing process.
提出了一种基于多流形的数据分类算法，算法大致分为两步：学习过程和测试过程。
For text classification based on SVM learning algorithm, usually there is an abundance of training data, which will cost a lot of computing resources in training process.
在采用SVM算法的文本分类中，由于文本所表征的向量空间维数通常非常巨大，因此在训练过程中将耗费大量的系统资源。
Compare to other learning algorithm, SVM learning algorithm performances better in text classification.
相对于其他学习算法，SVM在文本的分类中表现出了更优异的性能。
Locality Preserving Projections algorithm (LPP) is a new dimensionality reduction technique. But it is an unsupervised learning algorithm. It could not process classification effectively.
局部保持投影(LPP)是一种新的数据降维技术，但其本身是一种非监督学习算法，对于分类问题效果不是太好。
The supervised learning algorithm was usually used for remote sensing image classification, but its training samples need to be chosen by manual, which was boring and sometimes even difficult.
遥感图像分类方法通常采用监督的学习算法，它需要人工选取训练样本，比较繁琐，而且有时很难得到；而非监督学习算法的分类精度通常很难令人满意。
The supervised learning algorithm was usually used for remote sensing image classification, but its training samples need to be chosen by manual, which was boring and sometimes even difficult.
遥感图像分类方法通常采用监督的学习算法，它需要人工选取训练样本，比较繁琐，而且有时很难得到；而非监督学习算法的分类精度通常很难令人满意。
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