Essays about: "multilayer perception MLP"

Found 3 essays containing the words multilayer perception MLP.

1. 1. A Machine Learning Approach to Skin Cancer Delineation on Photoacoustic Imaging

   University essay from Lunds universitet/Fysiska institutionen; Lunds universitet/Beräkningsbiologi och biologisk fysik - Genomgår omorganisation

   Author : Alice Fracchia; [2023]
   Keywords : Skin cancer; carcinoma; photoacoustic imaging; ultrasound imaging; machine learning; dimensionality reduction; sandpiles algorithm; active contour; multilayer perception MLP ; convolutional neural networks CNN ; autoencoder; Physics and Astronomy;

   Abstract : Skin cancer is a growing public health concern due to its prevalence among the population. Current clinical procedures require high invasiveness and multiple surgeries, which are responsible for patient discomfort and high medical expenses. READ MORE

3. 2. Using Reinforcement Learning to Correct Soft Errors of Deep Neural Networks

   University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)

   Author : Yuhang Li; [2023]
   Keywords : DNN; Soft errors; Redundancy; DRL; DQN; Transfer learning; Training time; DNN; Mjuka fel; Redundans; DRL; DQN; Överföringsinlärning; Utbildningstid;

   Abstract : Deep Neural Networks (DNNs) are becoming increasingly important in various aspects of human life, particularly in safety-critical areas such as autonomous driving and aerospace systems. However, soft errors including bit-flips can significantly impact the performance of these systems, leading to serious consequences. READ MORE

4. 3. Credit risk analysis with machine learning techniques in peer-to-peer lending market

   University essay from Stockholms universitet/Företagsekonomiska institutionen

   Author : Mikael Nilsson; Qionglin Shan; [2018]
   Keywords : ;

   Abstract : After the sub-prime mortgage crisis of 2007 and global crisis of 2008, credit risk analysis has becomes more important than ever before. This paper conducts the credit risk analysis and compares classification performances among different algorithms (logistic regression, support vector machine, decision tree, multilayer perception, probabilistic neural network, Deep Learning) by using a large peer-to-peer lending dataset composed of a million observations. READ MORE