This paper presents the development of a Machine learning model through implementation of two algorithms namely logisticregression and Artificial Neural Networks to recognize handwritten digits from 0 to 9. The Train...
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ISBN:
(纸本)9781467385879
This paper presents the development of a Machine learning model through implementation of two algorithms namely logisticregression and Artificial Neural Networks to recognize handwritten digits from 0 to 9. The Training efficiency of both the algorithms is compared at the end of implementation. logisticregression is generally used for binary classification however;multiclass classification has been achieved by using One-vs-AII approach. Artificial neural networks are used for feed forward propagation to build the hypothesis function and back propagation is used for calculation of weights. The weights for both the models are minimized using advanced optimization algorithm such as fmiunc and fmincg. The formulas are implemented in vectorized format that is the formulas are solely expressed in matrix form and nowhere for loops are required. Vectorization does involve a lot of formulations to be done on paper beforehand but it ultimately serves the optimization purpose because higher programming languages such as MATLAB are very efficient to implement vectorized codes and this property should be exploited. The database consists of five thousand handwritten digits. The final result shows the program predicting the number on the display. The system is well trained and effective in recognizing the number.
This paper presents the development of a Machine learning model through implementation of two algorithms namely logisticregression and Artificial Neural Networks to recognize handwritten digits from 0 to 9. The Train...
详细信息
ISBN:
(纸本)9781467385886
This paper presents the development of a Machine learning model through implementation of two algorithms namely logisticregression and Artificial Neural Networks to recognize handwritten digits from 0 to 9. The Training efficiency of both the algorithms is compared at the end of implementation. logisticregression is generally used for binary classification however;multiclass classification has been achieved by using One-vs-All approach. Artificial neural networks are used for feed forward propagation to build the hypothesis function and back propagation is used for calculation of weights. The weights for both the models are minimized using advanced optimization algorithm such as fmiunc and fmincg. The formulas are implemented in vectorized format that is the formulas are solely expressed in matrix form and nowhere for loops are required. Vectorization does involve a lot of formulations to be done on paper beforehand but it ultimately serves the optimization purpose because higher programming languages such as MATLAB are very efficient to implement vectorized codes and this property should be exploited. The database consists of five thousand handwritten digits. The final result shows the program predicting the number on the display. The system is well trained and effective in recognizing the number.
Prognostic modeling is central to medicine, as it is often used to predict patients' outcome and response to treatments and to identify important medical risk factors. logisticregression is one of the most used a...
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ISBN:
(纸本)9781479975020
Prognostic modeling is central to medicine, as it is often used to predict patients' outcome and response to treatments and to identify important medical risk factors. logisticregression is one of the most used approaches for clinical prediction modeling. Traumatic brain injury (TBI) is an important public health issue and a leading cause of death and disability worldwide. In this study, we adapt CPXR (Contrast Pattern Aided regression, a recently introduced regression method), to develop a new logisticregression method called CPXR(Log), for general binary outcome prediction (including prognostic modeling), and we use the method to carry out prognostic modeling for TBI using admission time data. The models produced by CPXR(Log) achieved AUC as high as 0.93 and specificity as high as 0.97, much better than those reported by previous studies. Our method produced interpretable prediction models for diverse patient groups for TBI, which show that different kinds of patients should be evaluated differently for TBI outcome prediction and the odds ratios of some predictor variables differ significantly from those given by previous studies;such results can be valuable to physicians.
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