4 resultados para learning from failure

em Chinese Academy of Sciences Institutional Repositories Grid Portal


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Studies on learning problems from geometry perspective have attracted an ever increasing attention in machine learning, leaded by achievements on information geometry. This paper proposes a different geometrical learning from the perspective of high-dimensional descriptive geometry. Geometrical properties of high-dimensional structures underlying a set of samples are learned via successive projections from the higher dimension to the lower dimension until two-dimensional Euclidean plane, under guidance of the established properties and theorems in high-dimensional descriptive geometry. Specifically, we introduce a hyper sausage like geometry shape for learning samples and provides a geometrical learning algorithm for specifying the hyper sausage shapes, which is then applied to biomimetic pattern recognition. Experimental results are presented to show that the proposed approach outperforms three types of support vector machines with either a three degree polynomial kernel or a radial basis function kernel, especially in the cases of high-dimensional samples of a finite size. (c) 2005 Elsevier B.V. All rights reserved.

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A neural network-based process model is proposed to optimize the semiconductor manufacturing process. Being different from some works in several research groups which developed neural network-based models to predict process quality with a set of process variables of only single manufacturing step, we applied this model to wafer fabrication parameters control and wafer lot yield optimization. The original data are collected from a wafer fabrication line, including technological parameters and wafer test results. The wafer lot yield is taken as the optimization target. Learning from historical technological records and wafer test results, the model can predict the wafer yield. To eliminate the "bad" or noisy samples from the sample set, an experimental method was used to determine the number of hidden units so that both good learning ability and prediction capability can be obtained.

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Since the middle of 1980's, the mechanisms of transfer of training between cognitive subskills rest on the same body of declarative knowledge has been highly concerned. The dominant theory is theory of common element (Singley & Anderson, 1989) which predict that there will be little or no transfer between subskills within the same domain when knowledge is used in different ways, even though the subskills might rest on a common body of declarative knowledge. This idea is termed as "principle of use specificity of knowledge" (Anderson, 1987). Although this principle has gained some empirical evidence from different domains such as elementary geometry (Neves & Anderson, 1981) and computer programming (McKendree & Anderson, 1987), it is challenged by some research (Pennington et al., 1991; 1995) in which substantially larger amounts of transfer of training was found between substills that rest on a shared declarative knowledge but share little procedures (production rules). Pennington et al. (1995) provided evidence that this larger amounts of transfer are due to the elaboration of declarative knowledge. Our research provide a test of these two different explanation, by considering transfer between two subskills within the domain of elementary geometry and elementary algebra respectively, and the inference of learning method ("learning from examples" and "learning from declarative-text") and subject ability (high, middle, low) on the amounts of transfer. Within the domain of elementary geometry, the two subskills of generating proofs" (GP) and "explaining proofs" (EP) which are rest on the declarative knowledge of "theorems on the characters of parallelogram" share little procedures. Within the domain of elementary algebra, the two subskills of "calculation" (C) and "simplification" (S) which are rest on the declarative knowledge of "multiplication of radical" share some more procedures. The results demonstrate that: 1. Within the domain of elementary geometry, although little transfer was found between the two subskills of GP and EP within the total subjects, different results occurred when considering the factor of subject's ability. Within the high level subjects, significant positive transfer was found from EP to GP, while little transfer was found on the opposite direction (i. e. from GP to EP). Within the low level subjects, significant positive transfer was found from EP to GP, while significant negative transfer was found on the opposite direction. For the middle level subject, little transfer was found between the two subskills. 2. Within the domain of elementary algebra, significant positive transfer was found from S to C, while significant negative transfer was found on the opposite direction (i. e. from C to S), when considering the total subjects. The same pattern of transfer occurred within the middle level subjects and low level subject. Within the high level subjects, no transfer was found between the two subskills. 3. Within theses two domains, different learning methods yield little influence on transfer of training between subskills. Apparently, these results can not be attributed to either common procedures or elaboration of declarative knowledge. A kind of synthetic inspection is essential to construct a reasonable explanation of these results which should take into account the following three elements: (1) relations between the procedures of subskills; (2) elaboration of declarative knowledge; (3) elaboration of procedural knowledge. 排Excluding the factor of subject, transfer of training between subskills can be predicted and explained by analyzing the relations between the procedures of two subskills. However, when considering some certain subjects, the explanation of transfer of training between subskills must include subjects' elaboration of declarative knowledge and procedural knowledge, especially the influence of the elaboration on performing the other subskill. The fact that different learning methods yield little influence on transfer of training between subskills can be explained by the fact that these two methods did not effect the level of declarative knowledge. Protocol analysis provided evidence to support these hypothesis. From this research, we conclude that in order to expound the mechanisms of transfer of training between cognitive subskills rest on the same body of declarative knowledge, three elements must be considered synthetically which include: (1) relations between the procedures of subskills; (2) elaboration of declarative knowledge; (3) elaboration of procedural knowledge.

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This study designed tow experiments to explore the effect of two presentation forms(liner presentation and concept-map navigation presentation)on the student's learning process. Using the method of protocal analysis and the learning path records of these students, the author of this paper further analysed the information-processing process of these students. The main results showed as follows: (1) In the initial study phase, the main effects of both the presentation form and the learner type were obvious, and the interaction effect of these two variables was also obvious. Contrasting with the liner presentation form, the concept map navigation form interfered with the learning process of the student, especially the learning-disabled students. (2) There was a significant difference between excellent students and learning-disabled learning-disabled on self explanations amount. Excellent students produced more self- explanations than learning-disabled students, especially on two phases of learning from the the example and the conclusion. (3) Under the same learning path, the main effect of the learner type variable was obvious, the main effect of the presentation form and interaction effect of these two variables weren't obvious. But the liner presentation grouped still acquired a better result than the concept-map navigation presentation groupe.