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随着高等教育规模的扩大,学业表现不良逐渐成为一个不容忽视的现象,对学业表现不良的学生进行预测并提早给予干预可降低退学率并减少教育资源的损失。由于导致学业表现不良的因素众多且关系复杂,传统的基于相关分析的研究方法很难建立早期预测模型并进行应用。本研究旨在利用机器学习算法,对数据进行挖掘,并建立学业表现预测模型。研究对653名大一新生的心理健康状况、应对方式、人格、内外控倾向和相关人口统计学信息进行了收集,并在一年后采集了其学业成绩,利用随机森林(RF)、K邻近(KNN)、支持向量机(SVM)、决策树(DT)、朴素贝叶斯(NB)等机器学习算法建立了分类模型。结果显示,随机森林算法在识别学业表现不良学生时有最好的表现,其中准确率95.86%, 召回率91.83%,f1分数为93.80%。特征重要性分析显示,前10个对模型有最高贡献度的特征包括:年龄、性别、是否为独生子、内外控倾向、神经质倾向、积极应对倾向、宜人性倾向、一般症状指数、开放性倾向和焦虑水平。为避免过度拟合问题,本研究在一年后收集的166名新生样本中进行了模型验证,结果显示模型在新数据集上有较好的泛化表现,其中f1分数90.90%,准确率92.60%,召回率89.26%。研究提示基于人口统计学和心理测评信息,机器学习算法有助于及早识别学业表现不良学生并为开展早期干预提供启示。 相似文献
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In this paper we propose an approach which makes it possible to search non-dominated and only non-dominated solutions in multiple-objective linear programming. The approach is based on the use of a reference direction and lexicographic parametric programming. The requisite theory is developed in the paper. The use of the approach is illustrated with a numerical example. 相似文献
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N. N. Kudryavtseva 《Aggressive behavior》1991,17(5):285-291
The technique for simultaneous development of aggressive and submissive behaviors as a result of successive experiences of defeats or victories in daily intermale confrontations in male mice permanently living under sensory contact conditions is offered for behavioral, pharmacological, and neurophysiological studies of mechanisms of agonistic social relations. Distant sensory contact is achieved by placing a pair of males into a common cage separated by a transparent partition with holes permitting visual contact and the individuals perceiving each other's odors but preventing any physical at contact all times except for 10-min daily tests. These conditions essentially elicit aggression in winner males and quickly result in submission by losers of the same strain of mice. The meaning of consecutive stages of the technique, the problem of controls, and applications of this model are discussed. 相似文献
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A new multiobjective linear programming (MOLP) algorithm is presented. The algorithm uses a variant of Karmarkar's interior-point algorithm known as the affine-scaling primal algorithm. Using this single-objective algorithm, interior search directions are generated and used to provide an approximation to the gradient of the (implicitly known) utility function. The approximation is guided by assessing locally relevant preference information for the various interior directions through interaction with a decision maker (DM). The resulting algorithm is an interactive approach that makes its progress towards the solution through the interior of the constraints polytope. 相似文献
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D. Caroline Blanchard Nina K. Popova Irina Z. Plyusnina Irina L. Velichko Desiree Campbell Robert J. Blanchard Julia Nikulina Ella M. Nikulina 《Aggressive behavior》1994,20(5):387-397
Selective breeding of wild rats over many generations on the basis of low or high defensive threat and attack to human approach and contact has produced highly polarized “domesticated” and “wild-type” animals. Because the selection procedure selectively involves these two defense patterns, and these clearly differ in the two groups, it is of interest to determine if other, nonselected, defensive behaviors to threat stimuli also change. “Domesticated” and “wild-type” rats of the thirty-fifth generation were run in a fear defense test battery (F/DTB) to systematically evaluate defensive behaviors to a variety of present threat stimuli. “Domesticated” rats showed reduced avoidance and slower flight speed to an approaching experimenter, reduced jump/startle response to handelap and dorsal contact, less vocalization and boxing to vibrissae stimulation or to an anesthetized conspecific, and reduced defensiveness to an attempted pickup by the experimenter. These results indicate that selective bi-directional breeding for defensive threat and attack to human approach and contact produces group differences in a variety of defensive behaviors, and in defensiveness to stimuli other than those on which the selection was based. © 1994 Wiley-Liss, Inc. 相似文献
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Jiin-huarng Guo Wei-ming Luh 《The British journal of mathematical and statistical psychology》2020,73(2):316-332
The equality of two group variances is frequently tested in experiments. However, criticisms of null hypothesis statistical testing on means have recently arisen and there is interest in other types of statistical tests of hypotheses, such as superiority/non-inferiority and equivalence. Although these tests have become more common in psychology and social sciences, the corresponding sample size estimation for these tests is rarely discussed, especially when the sampling unit costs are unequal or group sizes are unequal for two groups. Thus, for finding optimal sample size, the present study derived an initial allocation by approximating the percentiles of an F distribution with the percentiles of the standard normal distribution and used the exhaustion algorithm to select the best combination of group sizes, thereby ensuring the resulting power reaches the designated level and is maximal with a minimal total cost. In this manner, optimization of sample size planning is achieved. The proposed sample size determination has a wide range of applications and is efficient in terms of Type I errors and statistical power in simulations. Finally, an illustrative example from a report by the Health Survey for England, 1995–1997, is presented using hypertension data. For ease of application, four R Shiny apps are provided and benchmarks for setting equivalence margins are suggested. 相似文献
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Jason D. Rights Kristopher J. Preacher David A. Cole 《The British journal of mathematical and statistical psychology》2020,73(Z1):194-211
In the multilevel modelling literature, methodologists widely acknowledge that a level-1 variable can have distinct within-cluster and between-cluster effects, and that failing to disaggregate these can yield a slope estimate that is an uninterpretable, conflated blend of the two. Methodologists have stated, however, that including conflated slopes of level-1 variables in a model is not problematic if substantive interest lies only in effects of level-2 predictors. Researchers commonly follow this advice and use methods that do not disaggregate effects of level-1 control variables (e.g., grand mean centering) when examining effects of level-2 predictors. The primary purpose of this paper is to show that this is a dangerous practice. When level-specific effects of level-1 variables differ, failing to disaggregate them can severely bias estimation of level-2 predictor slopes. We show mathematically why this is the case and highlight factors that can exacerbate such bias. We corroborate these findings with simulations and present an empirical example, showing how such distortions can severely alter substantive conclusions. We ultimately recommend that simply including the cluster mean of the level-1 variable as a control will alleviate the problem. 相似文献