Essays about: "Linear Support Vector Classifier"

Showing result 1 - 5 of 21 essays containing the words Linear Support Vector Classifier.

1. 1. Predicting Risk Level in Life Insurance Application : Comparing Accuracy of Logistic Regression, DecisionTree, Random Forest and Linear Support VectorClassifiers

   University essay from Blekinge Tekniska Högskola/Institutionen för datavetenskap

   Author : Pulagam Karthik Reddy; Sutapalli Veerababu; [2023]
   Keywords : Decision Tree Classifier; Logistic Regression; Machine Learning; Random Forest Classifier; Linear Support Vector Classifier;

   Abstract : Background: Over the last decade, there has been a significant rise in the life insurance industry. Every life insurance application is associated with some level ofrisk, which determines the premium they charge. The process of evaluating this levelof risk for a life insurance application is time-consuming. READ MORE

3. 2. Predicting Customer Churn and Customer Lifetime Value (CLV) using Machine Learning

   University essay from Lunds universitet/Matematisk statistik

   Author : Magnus Gerde; [2023]
   Keywords : Churn; CLV; SVM; Logistic Regression; Linear Regression; Random Forest Model; Mathematics and Statistics;

   Abstract : In an evermore competitive environment for companies and business, predictive customer behaviour models can give companies a competitive edge over its competitors. Two such important predictive behaviour models are customer churn models and customer lifetime value (CLV) models. READ MORE

4. 3. Head impact detection with sensor fusion and machine learning

   University essay from Luleå tekniska universitet/Institutionen för system- och rymdteknik

   Author : Aron Strandberg; [2022]
   Keywords : head impact detection; sensor fusion; 3D; machine learning; support vector machine; random forests;

   Abstract : Head injury is common in many different sports and elsewhere, and is often associated with differentdifficulties. One major problem is to identify and value the injury or the severity. Sometimes there is no sign of head injury, but a serious neck distortion has occurred, causing similar symptoms as head injuries e.g. READ MORE

5. 4. Evaluating Feature Selection Methods for Automated Computer Based Diagnostics of Diabetes

   University essay from KTH/Datavetenskap

   Author : Erik Wachtmeister; Noel Karlsson Johansson; [2022]
   Keywords : ;

   Abstract : Diabetes affects roughly 8% of the world population over the age of 18 and causes approximately 5 million deaths for people over the age of 20 each year. Furthermore, maybe half of all people with diabetes are undiagnosed. They could be diagnosed with automated computer-based diagnostics, but machine learning has its challenges. READ MORE

6. 5. E-noses equipped with Artificial Intelligence Technology for diagnosis of dairy cattle disease in veterinary

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

   Author : Farbod Haselzadeh; [2021]
   Keywords : Artificial intelligence; Electronic nose; Gas sensor arrays; Principal component analysis; Autoencoder; Veterinary diagnose; Feature extraction; Dimentionality reduction; Normalization; Maskin intelligence; Artificial intelligence; Elektronisk näsa; Gas sensore array; Normalisering; dimensionalitetsminskning; Autoencoder; Klassificering AI; E-nose; Feature Extraction; Normalization; PCA; Autoencoder; Encoder; Decoder; MLP; Classifier; LDA; Support Vector Machine; Logistic Regression; Cross Validation; Signal segmentation;

   Abstract : The main goal of this project, running at Neurofy AB, was that developing an AI recognition algorithm also known as, gas sensing algorithm or simply recognition algorithm, based on Artificial Intelligence (AI) technology, which would have the ability to detect or predict diary cattle diseases using odor signal data gathered, measured and provided by Gas Sensor Array (GSA) also known as, Electronic Nose or simply E-nose developed by the company. Two major challenges in this project were to first overcome the noises and errors in the odor signal data, as the E-nose is supposed to be used in an environment with difference conditions than laboratory, for instance, in a bail (A stall for milking cows) with varying humidity and temperatures, and second to find a proper feature extraction method appropriate for GSA. READ MORE