scholarly journals Model-Based Machine Learning for Joint Digital Backpropagation and PMD Compensation

2020 ◽  
Author(s):  
Christian Häger ◽  
Henry D. Pfister ◽  
Rick M. Bütler ◽  
Gabriele Liga ◽  
Alex Alvarado
Keyword(s):  
Machine Learning ◽  
Model Based ◽  
Pmd Compensation

scholarly journals Model-Based Machine Learning for Joint Digital Backpropagation and PMD Compensation

2020 ◽  
pp. 1-1
Author(s):  
Rick M. Butler ◽  
Christian Hager ◽  
Henry D. Pfister ◽  
Gabriele Liga ◽  
Alex Alvarado
Keyword(s):  
Machine Learning ◽  
Model Based ◽  
Pmd Compensation

scholarly journals Field-Scale Soil Moisture Retrieval Using PALSAR-2 Polarimetric Decomposition and Machine Learning

Agronomy ◽  
2020 ◽  
Vol 11 (1) ◽  
pp. 35
Author(s):  
Xiaodong Huang ◽  
Beth Ziniti ◽  
Michael H. Cosh ◽  
Michele Reba ◽  
Jinfei Wang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Soil Moisture ◽  
Canopy Cover ◽  
The United States ◽  
Optical Data ◽  
Study Region ◽  
Model Based ◽  
Key Indicator ◽  
Ground Measurement ◽  
L Band

Soil moisture is a key indicator to assess cropland drought and irrigation status as well as forecast production. Compared with the optical data which are obscured by the crop canopy cover, the Synthetic Aperture Radar (SAR) is an efficient tool to detect the surface soil moisture under the vegetation cover due to its strong penetration capability. This paper studies the soil moisture retrieval using the L-band polarimetric Phased Array-type L-band SAR 2 (PALSAR-2) data acquired over the study region in Arkansas in the United States. Both two-component model-based decomposition (SAR data alone) and machine learning (SAR + optical indices) methods are tested and compared in this paper. Validation using independent ground measurement shows that the both methods achieved a Root Mean Square Error (RMSE) of less than 10 (vol.%), while the machine learning methods outperform the model-based decomposition, achieving an RMSE of 7.70 (vol.%) and R2 of 0.60.

scholarly journals MODES: model-based optimization on distributed embedded systems

2021 ◽  
Author(s):  
Junjie Shi ◽  
Jiang Bian ◽  
Jakob Richter ◽  
Kuan-Hsun Chen ◽  
Jörg Rahnenführer ◽  
...  
Keyword(s):  
Machine Learning ◽  
Embedded Systems ◽  
Learning Model ◽  
Black Box ◽  
Distributed Embedded Systems ◽  
Data Set ◽  
Individual Model ◽  
Model Based ◽  
Machine Learning Model ◽  
Distributed Machine Learning

AbstractThe predictive performance of a machine learning model highly depends on the corresponding hyper-parameter setting. Hence, hyper-parameter tuning is often indispensable. Normally such tuning requires the dedicated machine learning model to be trained and evaluated on centralized data to obtain a performance estimate. However, in a distributed machine learning scenario, it is not always possible to collect all the data from all nodes due to privacy concerns or storage limitations. Moreover, if data has to be transferred through low bandwidth connections it reduces the time available for tuning. Model-Based Optimization (MBO) is one state-of-the-art method for tuning hyper-parameters but the application on distributed machine learning models or federated learning lacks research. This work proposes a framework $$\textit{MODES}$$ MODES that allows to deploy MBO on resource-constrained distributed embedded systems. Each node trains an individual model based on its local data. The goal is to optimize the combined prediction accuracy. The presented framework offers two optimization modes: (1) $$\textit{MODES}$$ MODES -B considers the whole ensemble as a single black box and optimizes the hyper-parameters of each individual model jointly, and (2) $$\textit{MODES}$$ MODES -I considers all models as clones of the same black box which allows it to efficiently parallelize the optimization in a distributed setting. We evaluate $$\textit{MODES}$$ MODES by conducting experiments on the optimization for the hyper-parameters of a random forest and a multi-layer perceptron. The experimental results demonstrate that, with an improvement in terms of mean accuracy ($$\textit{MODES}$$ MODES -B), run-time efficiency ($$\textit{MODES}$$ MODES -I), and statistical stability for both modes, $$\textit{MODES}$$ MODES outperforms the baseline, i.e., carry out tuning with MBO on each node individually with its local sub-data set.

Marketing customer response scoring model based on machine learning data analysis

2020 ◽  
pp. 1-11
Author(s):  
Tang Yan ◽  
Li Pengfei
Keyword(s):  
Machine Learning ◽  
Data Analysis ◽  
Data Extraction ◽  
Machine Learning Algorithms ◽  
Customer Relationship ◽  
Customer Data ◽  
Modeling And Analysis ◽  
Scoring Model ◽  
Model Based ◽  
Learning Data

In marketing, problems such as the increase in customer data, the increase in the difficulty of data extraction and access, the lack of reliability and accuracy of data analysis, the slow efficiency of data processing, and the inability to effectively transform massive amounts of data into valuable information have become increasingly prominent. In order to study the effect of customer response, based on machine learning algorithms, this paper constructs a marketing customer response scoring model based on machine learning data analysis. In the context of supplier customer relationship management, this article analyzes the supplier’s precision marketing status and existing problems and uses its own development and management characteristics to improve marketing strategies. Moreover, this article uses a combination of database and statistical modeling and analysis to try to establish a customer response scoring model suitable for supplier precision marketing. In addition, this article conducts research and analysis with examples. From the research results, it can be seen that the performance of the model constructed in this article is good.

Regional effect of monetary policy based on GVAR model and machine learning

2020 ◽  
pp. 1-13
Author(s):  
Zengming Zhao ◽  
Wenting Chen
Keyword(s):  
Machine Learning ◽  
Growth Rate ◽  
Monetary Policy ◽  
Stress Test ◽  
Analysis Model ◽  
Conduction Model ◽  
Regional Effect ◽  
Macroeconomic Factors ◽  
Model Based ◽  
Gray Correlation

Monetary policy is an important means for a country to regulate macroeconomic operations and achieve established economic goals. Moreover, a reasonable monetary policy improves the efficiency of financial operations on a global scale and effectively resolves the financial crisis. At present, scholars from various countries have begun to pay attention to the issue of differentiated formulation of monetary policy among regions. This paper combines machine learning to construct a monetary policy differentiation effect analysis model based on the GVAR model. Moreover, this paper uses the gray correlation analysis method to obtain the gray correlation matrix between industries, and then introduces the industry’s own characteristics, industry relevance and macroeconomic factors into the macro stress test of credit risk. In addition, this paper constructs a conduction model based on the industry GVAR model, and uses the first-order difference sequence of GDP growth rate, CPI growth rate and M2 growth rate of each economic region to construct a GVAR model to test the impulse response function. The results of the test show that the monetary policy shocks of various economic regions are significantly different. All in all, the research results show that the performance of the model constructed in this paper is good.

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