By mapping input data into a high dimensional characteristic space in which an optimal separating hyperplane is built , svm presents a lot of advantages for resolving the small samples , nonlinear and high dimensional pattern recognition , as well as other machine - learning problems such as function fitting Svm的基本思想是通过非线性变换将输入空间变换到一个高维空间，然后在这个新的空间中求取最优分类超平面。它在解决小样本、非线性及高维模式识别问题中表现出许多特有的优势，并能够推广应用到函数拟合等其他机器学习问题中。
The relationship among stressors , coping styles and mental health is close and the relationship coefficient is very high . 3 . the major stressors facing the hpe students are employment , study , life and social life . life and social life poses the greatest impact on the mental health of the students 本研究结论：自编《高职生心理应激源量表》具有较好的信、效度，达到了心理测量学要求；应激源、应付方式与心理健康之间有密切关系，且相关系数达到显著性水平；高职生面临的主要应激源为就业问题、学习问题，对心理健康影响最大的因素是生活与社交问题。
They are agricultural productive materials price growth rate , sown area of grain crops growth rate , grain yield per area growth rate - , natural disaster covered grain areas growth rate , net grain import change rate , grain reserve change rate , population growth rate , per income growth rate , city and town population growth rate , food industry production value growth rate , year - end pig number growth rate , medical & pharmaceutical and textile industry production value growth rate , grain marketization degree , inflation rate using the previous year as base year ( preceding year = 100 ) , public grain purchases price growth rate , investment in agricultural science and technology growth rate , investment in agricultural infrastructure growth rate , growth rate of graduates number from agriculture , forestry , science & technology universities and colleges and specialized secondary schools , government expenditure for agriculture and agricultural credit growth rate , international grain price growth rate , rmb exchange rate growth rate , last grain price growth rate , economic crop price growth rate , , meanwhile , a new method is attempted to be used in this paper and the grain price early - warning problem is transformed into machine learning problem by introducing statistic learning theory and svm method which are gaining popularity in machine learning field at present in the world 在此基础上，筛选出23个警兆指标：农用生产资料价格增长率、粮食播种面积增长率、粮食单产增长率、粮食受灾面积增长率、粮食净进口量变化率、粮食储备变动率、人口增长率、人均收入增长率、城镇人口增长率、食品工业产值增长率、猪年末头数增长率、医药纺织工业产值增长率、粮食市场化程度、以上年为基年的通货膨胀率、国家粮食定购价格增长率、农业科技投入增长率、农业基础设施投入增长率、农、林、科技高校大、中专毕业生人数增长率、财政支农资金比重及农业信贷增长率、国际粮食市场价格增长率、人民币汇率增长率、上期粮食价格增长率、经济作物价格增长率。同时论文在预警方法上作了新的尝试，把粮食价格预警问题转换成一个机器学习问题，引进当前国际上机器学习领域中比较热门的统计学习理论和支持向量机方法，用顺序回归算法对历史数据进行学习建立了粮食价格预警模型。
Statistical learning theory ( slt ) is based on the structural risk minimization ( srm ) principle , and it is a new set of theory system , which specially aims at machine learning issues under the circumstances of small - sample . based on this slt , supporting vector machine ( svm ) method has been developed as a new machine learning algorithm and also practical applications of slt 统计学习理论建立在结构风险最小化原则基础上,它是专门针对少样本情况下机器学习问题而建立的一套新的理论体系,支持向量机就是在统计学习理论这一基础上发展起来的一种新的机器学习算法,它是统计学习理论的具体应用。
Slt provides us a new framework for the general learning problem . a large number of experiments have shown that svm has not only simple structure , but also better performances , especially better generalization ability . svm can solve small - sample learning problem better , and it is a completely new neural network technique 实践表明，建立在( slt )之上支持向量机不仅结构简单，而且技术性能尤其是推广能力明显提高，能够解决好大量现实中的小样本学习问题，它是一个全新的神经网络技术。
So this paper mainly studies some cooperative learning problems related to mathematics teaching in secondary schools , such as grouping in cooperative learning , activities , choice of the right moment , mathematical problems suitable for cooperative learning , ways to foster students " communication and cooperation in groups . this research bases itself upon chinese classroom - instructed style 为此本文研究的对象主要是与中学数学教学应用有关的合作学习问题，如合作学习活动中小组的编排；合作学习的时机选择；适合小组合作学习数学问题的选择和构建；学生在小组中的合作与交流的促进和保障等。
It & the education reform in k12 abstract : in ancient , the original technology has been accumulated by the basic needs of human being , and it promotes the produce and development of science technology . about bc 600 - 400 , such scholars as confucius and socrates started to research the topic about learning , this result in the spread of technology and learning theory developing alternately 人类生活的早期，基本的生活需要推动了原始技术的积累，并进一步推动了科学技术的产生与发展，到公元前六世纪末至前四世纪间，以孔子、苏格拉底等为代表的中西方学者开始了早期有关学习问题的理论研究，使得技术的传播与学习理论得到交替发展。
Being different from traditional neural network or nn , nn is based on traditional statistics , which provides conclusion only for the situation where sample size is tending to infinity , while svm is based on statistical learning theory or slt , which is a small - sample statistics and concerns mainly the statistic principles when sample are limited , especially the properties of learning procedure 支持向量机( svm )是九十年代中期发展起来的新的机器学习技术，与传统的神经网络( nn )技术不同， svm是以统计学习理论( slt )为基础， nn是以传统统计学理论为基础。传统统计学的前提条件是要有足够多的样本，而统计学习理论是着重研究小样本条件下的统计规律和学习方法的，它为机器学习问题建立了一个很好的理论框架。
