Essays about: "Linear Support Vector Classification"

Showing result 1 - 5 of 49 essays containing the words Linear Support Vector Classification.

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. Individual revenue forecasting in the banking sector

   University essay from Lunds universitet/Nationalekonomiska institutionen; Lunds universitet/Statistiska institutionen

   Author : Ricardo Jorge Pinto Brandão; Simona Sulzickyte; [2023]
   Keywords : Revenue forecasting; Banking; Machine Learning; XGBoost; Customer segmentation; Business and Economics;

   Abstract : This paper analyses data from a Swedish bank combined with macroeconomic indicators to forecast revenues for individual customers over the course of four years. Separate models are created for recurring customers and customers who have just joined the bank. READ MORE

4. 3. Predicting the size of a company winning a procurement: an evaluation study of three classification models

   University essay from Uppsala universitet/Statistiska institutionen

   Author : Ellen Björkegren; [2022]
   Keywords : public procurement; classification; Linear Discriminant Analysis; Random Forests; Support Vector Machines;

   Abstract : In this thesis, the performance of the classification methods Linear Discriminant Analysis (LDA), Random Forests (RF), and Support Vector Machines (SVM) are compared using procurement data to predict what size company will win a procurement. This is useful information for companies, since bidding on a procurement takes time and resources, which they can save if they know their chances of winning are low. READ MORE

5. 4. Predicting basketball performance based on draft pick : A classification analysis 

   University essay from Uppsala universitet/Statistiska institutionen

   Author : Fredrik Harmén; [2022]
   Keywords : machine learning; linear discriminant analysis; k-nearest neighbors; support vector machines; random forests;

   Abstract : In this thesis, we will look to predict the performance of a basketball player coming into the NBA depending on where the player was picked in the NBA draft. This will be done by testing different machine learning models on data from the previous 35 NBA drafts and then comparing the models in order to see which model had the highest accuracy of classification. 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