Titlebook: Machine Learning and Data Mining in Pattern Recognition; 13th International C Petra Perner Conference proceedings 2017 Springer Internation

平項(xiàng)山

Initiative

HPA533

分離

神刊

Over-Fitting in Model Selection with Gaussian Process Regression,which allows flexible customization of the GP to the problem at hand. An oft-overlooked issue that is often encountered in the model process is over-fitting the model selection criterion, typically the marginal likelihood. The over-fitting in machine learning refers to the fitting of random noise pr

ostensible

exhibit

Anomaly Detection from Kepler Satellite Time-Series Data,s. Windowed mean division normalization is presented as a method to transform non-linear data to linear data. Modified Z-score, general extreme studentized deviate, and percentile rank algorithms were applied to initially detect anomalies. A refined windowed modified Z-score algorithm was used to de

Locale

Prediction of Insurance Claim Severity Loss Using Regression Models,nal data used for this research work is obtained from Allstate insurance company which consists of 116 categorical and 14 continuous predictor variables. We implemented Linear regression, Random forest regression (RFR), Support vector regression (SVR) and Feed forward neural network (FFNN) for this

guardianship

幸福愉悅感

Vulnerability of Deep Reinforcement Learning to Policy Induction Attacks, work, we establish that reinforcement learning techniques based on Deep Q-Networks (DQNs) are also vulnerable to adversarial input perturbations, and verify the transferability of adversarial examples across different DQN models. Furthermore, we present a novel class of attacks based on this vulner