Learning from errors in a driving simulation: effects on driving skill and self-confidence

Two experiments investigated the effect of making errors during training (error training) on a driving simulator versus learning from examples of errors (guided error training) on driving skill and confidence. Experiment 1 indicated that compared with errorless learning (where participants drove through a training run not designed to elicit errors), error training led to significantly better transfer to driving tests that were analogous to those situations encountered in training and more effective use of strategies for coping with a novel driving situation. Error training also reduced self-confidence in driving skill at the end of training relative to errorless learning. Experiment 2 provided weak evidence of the superiority of guided error training over errorless learning (where the driver in the video did not make any errors) on analogous tests, and no evidence of transfer to a novel test. Furthermore, guided error training did not influence self-confidence in driving skill. The potential value of driving simulators in providing active processing during driver training is discussed, along with the effects of passive and active exposure to errors on driver confidence.     

数据挖掘技术在教学评价中的应用

数据挖掘技术是在海量数据中提取有用信息的有效手段,它被广泛应用于经济效益巨大的金融、商业等领域,而在教学评价中应用较少,这使得一些对教学效果有较大影响的因素往往被忽视。该文介绍了把数据挖掘技术引入网上教学评价中的有益尝试。     

基于置信度的手写体数字识别多分类器动态组合

多分类器组合利用不同分类器、不同特征之间的互补性，提高了组合分类器的识别率。传统的组合方法里，各分类器在组合中所承担的角色是固定的，而实际应用中，对于不同的测试样本，每个分类器识别结果的可信度是不同的。该文根据分类器置信度理论，提出了各类别的置信度。用测试样本自身的置信度信息实现分类器的动态组合，并把这种动态组合方法具体应用到手写体数字的识别。这种方法还可以在不影响已有数据的情况下添加新的分类器进行组合。     

Implementing the Double Bootstrap

The single bootstrap already is popular in economics, though the double bootstrap has better convergence properties. We discuss the theory and implementation of the double bootstrap, both with and without the pivotal transformation, and give detailed examples of each. One example is a nonlinear double bootstrap of a Cobb-Douglas production function, and explains the use of Gauss-Newton Regressions as a device to decrease computational time. Another example is double bootstrapping elasticities from a translog production function.     

Subjective Probability Intervals: How to Reduce Overconfidence by Interval Evaluation.

Format dependence implies that assessment of the same subjective probability distribution produces different conclusions about over- or underconfidence depending on the assessment format. In 2 experiments, the authors demonstrate that the overconfidence bias that occurs when participants produce intervals for an uncertain quantity is almost abolished when they evaluate the probability that the same intervals include the quantity. The authors successfully apply a method for adaptive adjustment of probability intervals as a debiasing tool and discuss a tentative explanation in terms of a naive sampling model. According to this view, people report their experiences accurately, but they are naive in that they treat both sample proportion and sample dispersion as unbiased estimators, yielding small bias in probability evaluation but strong bias in interval production. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

ADB610钢冲击功的概率模型及安全冲击功研究

鉴于ADB610钢冲击功(Akv)数据的分散性。采用多种概率统计模型对其进行拟合,并采用相关系数法和柯尔莫哥洛夫～斯米尔诺夫检验法(K-S检验法)检验拟合效果,检验结果显示三参数威布尔分布最优。通过对ADB610钢冲击功置信区间的研究,得到置信度95%、可靠度99.9%的置信区间为[139.5J,217.1J]。最后对冲击功进行求解,以获得一定可靠度下的安全冲击功,分别得到对应可靠度为0.9,0.975和0.99时的安全冲击功228.7J,210.1J和200.7 J。     

Moving object detection based on confidence factor and CSLBP features

Moving object detection is a challenging task in many computer vision applications. In this paper, we propose a robust background modelling method for this task. First, the background is updated by an adaptive strategy based on Centre-symmetric Local Binary Patterns. Then, background subtraction is used for detecting moving object. Although the traditional background subtraction technique uses the difference value between the current pixel and its corresponding background pixel for objection detection, our method utilises the confidence factor to determine whether the current pixel is a background or foreground pixel. The confidence factor of the current pixel is calculated in term of the difference values of its neighbourhood pixels. In the experiments, the proposed algorithm is tested on several challenging datasets such as PETS 2009, BMC 2012 and SABS. The experimental results demonstrate that our algorithm can robustly detect moving object under various scenes.     

轧机伺服液压缸内泄漏故障诊断研究

针对目前轧机伺服液压缸故障诊断过程中,故障特征提取困难,信号非线性变化,数据量大的问题,提出了一种基于深度置信网络的轧机伺服液压缸故障诊断的方法.根据轧机系统工作原理,建立轧机系统仿真模型,对轧机内泄漏故障状况进行模拟.利用深度置信网络在智能故障诊断的优越性,将信号归一化处理后放入深度置信网络进行训练,然后通过反向传播...     

基于逐步优化分类模型的跨领域文本情感分类

跨领域文本情感分类已成为自然语言处理领域的一个研究热点。针对传统主动学习不能利用领域间的相关信息以及词袋模型不能过滤与情感分类无关的词语,提出了一种基于逐步优化分类模型的跨领域文本情感分类方法。首先选择源领域和目标领域的公共情感词作为特征,在源领域上训练分类模型,再对目标领域进行初始类别标注,选择高置信度的文本作为分类模型的初始种子样本。为了加快目标领域的分类模型的优化速度,在每次迭代时,选取低置信度的文本供专家标注,将标注的结果与高置信度文本共同加入训练集,再根据情感词典、评价词搭配抽取规则以及辅助特征词从训练集中动态抽取特征集。实验结果表明,该方法不仅有效地改善了跨领域情感分类效果,而且在一定程度上降低了人工标注样本的代价。     

Fast Algorithms to Enumerate All Common Intervals of Two Permutations

Given two permutations of n elements, a pair of intervals of these permutations consisting of the same set of elements is called a common interval . Some genetic algorithms based on such common intervals have been proposed for sequencing problems and have exhibited good prospects. In this paper we propose three types of fast algorithms to enumerate all common intervals: (i) a simple O(n ² ) time algorithm (LHP), whose expected running time becomes O(n) for two randomly generated permutations, (ii) a practically fast O(n ² ) time algorithm (MNG) using the reverse Monge property, and (iii) an O(n+K) time algorithm (RC), where K is the number of common intervals. It will also be shown that the expected number of common intervals for two random permutations is O(1) . This result gives a reason for the phenomenon that the expected time complexity O(n) of the algorithm LHP is independent of K . Among the proposed algorithms, RC is most desirable from the theoretical point of view; however, it is quite complicated compared with LHP and MNG. Therefore, it is possible that RC is slower than the other two algorithms in some cases. For this reason, computational experiments for various types of problems with up to n=10 ⁶ are conducted. The results indicate that (i) LHP and MNG are much faster than RC for two randomly generated permutations, and (ii) MNG is rather slower than LHP for random inputs; however, there are cases in which LHP requires Ω(n ² ) time, but MNG runs in o(n ² ) time and is faster than both LHP and RC. Received December 21, 1996; revised June 2, 1998.