Sökning: "maskininlärning regression"

Visar resultat 1 - 5 av 269 uppsatser innehållade orden maskininlärning regression.

1. 1. ML implementation for analyzing and estimating product prices

   Kandidat-uppsats, Karlstads universitet/Institutionen för matematik och datavetenskap (from 2013)

   Författare :Abel Getachew Kenea; Gabriel Fagerslett; [2024]
   Nyckelord :Machine Learning; ML; Regression; Deep Learning; Artificial Neural Network; ANN; TensorFlow; ScikitLearn; CUDA; cuDNN; Estimation; Prediction; AI; Artificial Intelligence; Price Tracking; Price Logging; Price Estimation; Supervised Learning; Random Forest; Decision Trees; Batch Learning; Hyperparameter Tuning; Linear Regression; Multiple Linear Regression; Maskininlärning; Djup lärning; Artificiellt Neuralt Nätverk; Regression; TensorFlow; SciktLearn; ML; ANN; Estimation; Uppskattning; CUDA; cuDNN; AI; Artificiell Intelligens; pris loggning; pris estimation; prisspårning; Batchinlärning; Hyperparameterjustering; Linjär Regression; Multipel Linjär Regression; Supervised Learning; Random Forest; Decision Trees;

   Sammanfattning : Efficient price management is crucial for companies with many different products to keep track of, leading to the common practice of price logging. Today, these prices are often adjusted manually, but setting prices manually can be labor-intensive and prone to human error. LÄS MER

3. 2. Predicting Patent Data using Wavelet Regression and Bayesian Machine Learning

   Master-uppsats, KTH/Matematik (Avd.)

   Författare :Mattias Martinsen; [2023]
   Nyckelord :Wavelet; Regression; Bayesian network; Prediction; Patent; Machine Learning; Wavelet; Regression; Bayesiskt nätverk; Predicering; Patent; Maskininlärning;

   Sammanfattning : Patents are a fundamental part of scientific and engineering work, ensuringprotection of inventions owned by individuals or organizations. Patents areusually made public 18 months after being filed to a patent office, whichmeans that current publicly available patent data only provides informationabout the past. LÄS MER

4. 3. Radio Environment Compensation in a Narrowband IoT Positioning System : Using Radio Signal Metrics Between Stationary Devices

   Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)

   Författare :Elin Berglund; [2023]
   Nyckelord :Positioning; IoT; machine learning; range estimation; Positionering; IoT; maskininlärning; avståndsuppskattning;

   Sammanfattning : The Internet of Things (IoT) has emerged as a powerful tool for meeting our need to collect information about and interact with our environments. One important aspect of this technology is positioning which imposes requirements on both the energy consumption and the arrangement of the systems. LÄS MER

5. 4. Detection of insurance fraud using NLP and ML

   Master-uppsats, Lunds universitet/Matematisk statistik

   Författare :Rasmus Bäcklund; Hampus Öhman; [2023]
   Nyckelord :Technology and Engineering;

   Sammanfattning : Machine-Learning can sometimes see things we as humans can not. In this thesis we evaluated three different Natural Language Procces-techniques: BERT, word2vec and linguistic analysis (UDPipe), on their performance in detecting insurance fraud based on transcribed audio from phone calls (referred to as audio data) and written text (referred to as text-form data), related to insurance claims. LÄS MER

7. 5. Performance Benchmarking and Cost Analysis of Machine Learning Techniques : An Investigation into Traditional and State-Of-The-Art Models in Business Operations

   Kandidat-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)

   Författare :Jacob Lundgren; Sam Taheri; [2023]
   Nyckelord :Artificial Intelligence AI ; Machine Learning; Big Data; Natural Language Processing NLP ; Pre-Trained BERT; Fine-Tuned BERT; TF-IDF; Logistic Regression; Support Vector Machine SVM ; Cloud GPU; Operating Costs; Performance Efficiency; Business Intelligence;

   Sammanfattning : Eftersom samhället blir allt mer datadrivet revolutionerar användningen av AI och maskininlärning sättet företag fungerar och utvecklas på. Denna studie utforskar användningen av AI, Big Data och Natural Language Processing (NLP) för att förbättra affärsverksamhet och intelligens i företag. LÄS MER