The support vector machine (SVM) is an algorithm based on structure risk minimizing principle, having high generalization ability. In the course of multi-sensor information fusion of industrial control, sensor has big...
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The support vector machine (SVM) is an algorithm based on structure risk minimizing principle, having high generalization ability. In the course of multi-sensor information fusion of industrial control, sensor has bigger nonlinearity and fuzzy relation between coefficient and relevant parameter. A kind of model and algorithm of multiple sensor information fusion based on the support vector machine are proposed. The model offered a kind of effective way for little sample, non- linear, high dimension.
Since China entered the WTO, the accounting major has gradually integrated with international, and it is imperative to strengthen the English ability of accounting major. However, higher vocational colleges are limite...
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ISBN:
(纸本)9781629939919
Since China entered the WTO, the accounting major has gradually integrated with international, and it is imperative to strengthen the English ability of accounting major. However, higher vocational colleges are limited by the recruit students, and students' English foundation is poor. It has a lot of difficulty to directly carry on the English bilingual teaching, so the researches of bilingual teaching in higher vocational colleges have very important significance. This paper firstly investigates the students' English level of higher vocational college, and according to the investigation of the passing rate of cet4 and cet6, it finds that the English level of higher vocational colleges is low. This paper is based on the current situation to use the double variable control theory, and this method can have statistical analysis of the proportion of Chinese and English in the process of accounting teaching. It uses the least square method to optimize the data, and finally it obtains reasonable distribution proportion of English and Chinese in the teaching process. This paper is based on the related problems of accounting major’s bilingual teaching to put forward the opinion and strategy, and this can provide reliable theoretical basis for the implementation and development of bilingual teaching in higher vocational colleges.
We propose a novel kernel approach to dimension reduction for supervised learning: feature extraction and variable selection; the former constructs a small number of features from predictors, and the latter finds a su...
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ISBN:
(纸本)9781627480031
We propose a novel kernel approach to dimension reduction for supervised learning: feature extraction and variable selection; the former constructs a small number of features from predictors, and the latter finds a subset of predictors. First, a method of linear feature extraction is proposed using the gradient of regression function, based on the recent development of the kernel method. In comparison with other existing methods, the proposed one has wide applicability without strong assumptions on the regressor or type of variables, and uses computationally simple eigendecomposition, thus applicable to large data sets. Second, in combination of a sparse penalty, the method is extended to variable selection, following the approach by Chen et al. [2]. Experimental results show that the proposed methods successfully find effective features and variables without parametric models.
We discuss a methodology and the corresponding hardware architecture for performing self-calibration of analog/RF ICs through the use of on-die learning. More specifically, we introduce the design of an on-chip analog...
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ISBN:
(纸本)9781467384551
We discuss a methodology and the corresponding hardware architecture for performing self-calibration of analog/RF ICs through the use of on-die learning. More specifically, we introduce the design of an on-chip analog neural network which can be trained to implement a non-linear regression function. This regression function is, then, used to approximate a Figure-of-Merit (FoM) reflecting the performances of an analog/RF IC. As an input to this regression function, we use the readings of low-cost on-chip sensors in response to simple on-chip generated stimuli. The FoM is predicted for all possible settings of the knobs provided for calibrating the chip performances and the best option is retained. The proposed methodology is demonstrated on a tunable Low-Noise Amplifier (LNA) which was designed and fabricated in IBM's 130nm RF CMOS process. Experimental results show that the proposed self-calibration method achieves not only significant yield enhancement but also a compelling optimization of the LNA's overall performance across the entire chip population.
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