运用经典的贝叶斯决策方法将预抓取物体进行基本的三类抓、握、捏抓取模式分类。
The classical Bayes method is used in the classification of pre-grasp of multiple fingers based on three patterns which are grasping, holding and pinching.
决策树方法一直被用于贝叶斯决策问题的最优方案选择，即从若干决策方案中选择一个最优方案。
Up to now, the decision tree method is used to find the optimal solution of a Bayesian decision problem, that is, to select an optimal one from several decision alternatives.
在最大次品率准则下，应用了决策分析和贝叶斯方法，从而使本文的分析研究更具实际意义。
Under the law of maximum shoddy probability, it will have more practical significance for the probabilistic analysis of degree of compaction to use decision analysis and Bayesian method.
现有的分类预测的方法有许多种，常见的有决策树算法(C4.5)、贝叶斯分类算法、BP算法与支持向量机等。
There are many classification methods to forecast such as decision tree algorithm (C4.5), Bayes algorithm, BP algorithm and SVM.
在会计决策分析中所采用的先验概率通常由会计人员的主观判断来确定，使用贝叶斯方法能够对其进行修正，使之更加符合实际。
The prior probability in accounting decision is usually determined by the subjective judgment of the accountant. It can be modified by using the Bayes's method in order to be close to fact.
针对医学步态分析中的复杂场景下运动目标检测问题，提出了基于贝叶斯决策规则的方法。
This paper proposes a novel method for moving object detection from a video in medical gait analysis which contains not only stationary background objects but also moving background objects.
本文获得了刻度指数族变量带误差情形下的贝叶斯决策，且利用解卷积的核方法构造出了经验贝叶斯决策。
In this paper, a Bayes decision rule is derived for the scale-exponential family with error in variables, and an empirical Bayes (EB) decision rule is constructed by a deconvolution kernel method.
依据最小错误率贝叶斯决策理论，提出了一种基于最小错误率贝叶斯决策的图像分割方法。
Based on the minimum error Bayes decision theory, the authors proposed a new way of image (segmentation).
该算法综合了决策树方法和贝叶斯方法的优点，既有良好的可解释性，又有良好的增量学习能力。
The new algorithm combines the merit of decision tree induction method and naive Bayesian method. It retains the good interpretability of decision tree and has good incremental learning ability.
讨论了单纯使用BP神经网络作人脸的检测判定的不足，并在此基础上提出利用贝叶斯决策对神经网络的仿真结果进行第二次判定的方法。
Discussed the deficiency in face detection of BP neural network which was single used, and put forward a re-decision method by Bayesian decision.
讨论了单纯使用BP神经网络作人脸的检测判定的不足，并在此基础上提出利用贝叶斯决策对神经网络的仿真结果进行第二次判定的方法。
Discussed the deficiency in face detection of BP neural network which was single used, and put forward a re-decision method by Bayesian decision.
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