SOLO模型是对观察到的学习结果进行分类的理论框架，包括思维方式和复杂性水平两个关键特征。
SOLO model is a theory structure to classify the observed learning outcome with main features of models of thinking and levels of complexity.
本文主要研究目前较为流行的基于统计学习理论的分类方法——支持向量机方法(SVM)，以及小波变换提取特征的方法，将其用于人脸检测。
This paper mainly make research on classify methods based on statistical theory, support vector machine (SVM), and feature extraction method-wavelet transform, and using them in human face detection.
在统计学习理论中，尤其对于分类问题，VC维扮演着中心作用。
VC dimension plays a central role in the Statistical Learning Theory especially for classification problems.
利用粗糙集理论，通过对训练集的学习，构造分类规则，对支持向量机反馈后的结果再次进行处理。
By using rough set theory, this paper structures classification rules and processes the support vector machine feedback results with learning the train set.
本文依据知识分类和学习策略等心理学理论，对基于网络环境的初中语文教学内容呈现策略进行了系统设计研究。
The thesis is according to the Knowledge Classification Theory and Learning strategies, and design the presenting tactics of the Chinese web-based curriculum content of junior middle school.
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。
Support Vector Machines algorithm is a simple and effective classification method based upon statistical learning theory.
此外，根据研究目标文章还详细设计了知识管理系统中的知识分类系统、基于活动理论的虚拟学习社区。
Besides, the dissertation also detailedly design the knowledge taxon system in knowledge management system and virtual learning community based on activity theory.
课程实施需要与课程目标分类相匹配的学习理论的支撑。
Curriculum implementation requires the learning theories matching to the classification of curriculum goals.
其次，认真研究了统计学习理论的主要内容和SVM算法的基本原理，并且就SVM的多种多类别分类算法分别加以讨论。
Secondly, the text studies the Statistical Learning Theory(STL) and Support Vector Machine(SVM)theory seriously, discusses multi-category classification algorithms of SVM.
目标教学模式是在教育目标分类学和掌握学习的理论指导下，目前已经进行开发的教学模式之一，也是其它学科研究的热点问题。
Objective Teaching Model is one of the most mature teaching model ever developed, also it is the hot spot what is concerned about another branch of learning .
支持向量机是一种基于统计学习理论的新颖的机器学习方法，该方法已广泛用于解决分类和回归问题。
Support vector machines (SVM) are a kind of novel machine learning methods, based on statistical learning theory, which have been developed for solving classification and regression problems.
支持向量机是一种基于统计学习理论的新颖的机器学习方法，该方法已广泛用于解决分类和回归问题。
Support vector machines (SVM) are a kind of novel machine learning methods based on statistical learning theory, which has been developed to solve classification and regression problems.
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.
本文使用机器学习理论，讨论了用文本分类的方法来过滤垃圾邮件。
In this article we discussed filter spam methods using text categorization technologies in machine learning fields.
从某种意义上说，通过粗集理论挖掘出的分类规则是系统通过自学习机制而产生的，因而可以解决知识自动获取的瓶颈问题。
In a sense, rough sets is a kind of self-study mechanism, so we can solve the problem of knowledge obtained automatically by using rough sets.
实验室有关范畴学习的研究只局限于对分类的研究上，这就导致在分类研究基础上提出的范畴理论不能适用于其他非分类范畴学习任务。
Laboratory studies of category acquisition limit to categorization task. So category theory on the basis of classification study can not applied to other nonclassification types of category learning.
也将在课程中以带有稀疏值理论的分类神经网路与回归的使用来探讨监督式学习。
Supervised learning with the use of regression and classification networks with sparse data sets will be explored.
粗糙集理论最初在人工智能的某些分支，例如推理，自动分类，模式识别，学习算法等的研究中是很重要的。
The rough set concept can be of some importance, primarily in some branches of artificial intelligence, such as reasoning, automatic classification, pattern recognition, learning algorithms, etc.
文章前面三个部分研究了英语学习过程中错误的意义，错误的识别以及分类的理论。
The meaning and the identification of error in English study and the classification are studied in the first three parts.
依据数学习题在理论上的分类及功能，使编者应掌握习题的这些分类和功能，以便根据实际需要来编制习题。
The second part puts forward the theoretical classfication and instrction of mathematical exercises because the compiler could use it as he needs in fact.
本文通过对构件分类模式与检索技术的背景、研究现状及相关理论的学习，指出了目前基于刻面分类模式的构件检索方法中存在的问题。
By studying related background, current research work and theories, problems of the current component retrieval method based on faceted classification schema are pointed out.
提出了“惯性校正法”，对其能加速学习的原因进行了理论分析，并结合岩性分类和字母识别两个实验，对理论观点进行了验证。
The gradient decent learning with momentum is introduced, and analysis is described why it can speed up learning speed. Further more, two experiments are presented to verify previous views.
在分类器的设计上，重点讨论了最近邻分类器和基于统计学习理论的支持向量机(SVM)。
We emphases discussed the nearest neighbor classifier and support vector machine (SVM) based on the statistical study theory.
本文选择蛋白质二级结构数据为主要的研究对象，应用数据挖掘技术和机器学习中的动态规划理论进行蛋白质结构分类。
Protein second structure data is chosen as main study object, and data mining and dynamic programming are applied to protein structure classification.
支持向量机（SVM）是建立在统计学习理论基础上的一种小样本机器学习方法，用于解决二分类问题。
Support Vector Machines(SVM) are developed from the theory of limited samples Statistical Learning Theory (SLT) by Vapnik et al. , which are originally designed for binary classification.
通过客观分析当前运动学习的现状和误区，尝试进行运动学习层次分类，提出各层次学习心理模式并使之与行为主义联结学习理论、认知学习理论联系起来。
It attempts to classify the sports leaning structure for different learning steps, and furthermore to relate them with the behavioristic association theory and cognitive learning theory.
本研究的范围和内容主要涉及移动 学习概念界定、移动学习的分类、移动学习的特点、移动学习的主要理论依据、移动学习教学设计的原则和设计模式的建构等。
In this paper, the main research scope and content ofm-learning involved in definition classifying, speciality, main theoretical base, ID principles, establishing of ID model, and so on.
本研究的范围和内容主要涉及移动 学习概念界定、移动学习的分类、移动学习的特点、移动学习的主要理论依据、移动学习教学设计的原则和设计模式的建构等。
In this paper, the main research scope and content ofm-learning involved in definition classifying, speciality, main theoretical base, ID principles, establishing of ID model, and so on.
应用推荐