Sökning: "eXtreme Gradient Boosting XGBoost"

Visar resultat 1 - 5 av 15 uppsatser innehållade orden eXtreme Gradient Boosting XGBoost.

1. 1. COMPARATIVE ANALYSIS OF MACHINE LEARNING LOAD FORECASTING TECHNIQUES

   Magister-uppsats, Stockholms universitet/Institutionen för data- och systemvetenskap

   Författare :Humphry Takang Bate; [2023]
   Nyckelord :;

   Sammanfattning : Load forecasting plays a critical role in energy management, and power systems, enabling efficient resource allocation, improved grid stability, and effective energy planning and distribution. Without accurate very short term load forecasting, utility management companies face uncertain load patterns, unrealistic prices, and poor infrastructure planning. LÄS MER

3. 2. Machine Learning of Laser Ultrasonic Data to Predict Material Properties

   Master-uppsats, Linköpings universitet/Statistik och maskininlärning

   Författare :Filip Tuvenvall; [2023]
   Nyckelord :Machine Learning; Laser Ultrasonics; Material Properties; Steel; Hardness of Steel;

   Sammanfattning : The hardness of steel is an important quality parameter for several industrial applications. Conventional mechanical testing is used in quality testing for material hardness and the method is time-consuming, can cause material mix-ups, and results in material waste. LÄS MER

4. 3. Predicting Short-term Absences of a Railway Crew using Historical Data

   Master-uppsats, KTH/Matematisk statistik

   Författare :Agnes Björnfot; Sandra Fjelkestam; [2023]
   Nyckelord :statistics; machine learning; absence prediction; random forest; XGBoost; quantile regression; statistik; maskininlärning; frånvaroprognoser; random forest; XGBoost; kvantilregression;

   Sammanfattning : Transportation via train is considered the most environmentally friendly way of traveling and is widely seen as the future of transportation. Canceled and delayed trains worsen customer satisfaction; thus, punctual trains are crucial for railway companies. LÄS MER

5. 4. Estimation of Voltage Drop in Power Circuits using Machine Learning Algorithms : Investigating potential applications of machine learning methods in power circuits design

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

   Författare :Dimitrios Koutlis; [2023]
   Nyckelord :Voltage drop estimation; Application-specific Integrated Circuits ASICs ; Machine learning algorithms; XGBoost; Convolutional Neural Networks; Graph Neural Networks; Power circuit optimization; Uppskattning av spänningsfall; applikationsspecifika integrerade kretsar ASIC ; maskininlärningsalgoritmer; XGBoost; konvolutionella neurala nätverk; optimering av strömkretsar;

   Sammanfattning : Accurate estimation of voltage drop (IR drop), in Application-Specific Integrated Circuits (ASICs) is a critical challenge, which impacts their performance and power consumption. As technology advances and die sizes shrink, predicting IR drop fast and accurate becomes increasingly challenging. LÄS MER

7. 5. Modeling Melodic Accents in Jazz Solos

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

   Författare :Misael Berrios Salas; [2023]
   Nyckelord :Accents; Jazz Solo; Support Vector Regression SVR ; eXtreme Gradient Boosting XGBoost ; Multiple Linear Regression MLR ; Dynamic; Accenter; Jazz Solos; Support Vector Regression SVR ; eXtreme Gradient Boosting XGBoost ; Multiple Linear Regression MLR ; Dynamisk;

   Sammanfattning : This thesis looks at how accurately one can model accents in jazz solos, more specifically the sound level. Further understanding the structure of jazz solos can give a way of pedagogically presenting differences within music styles and even between performers. Some studies have tried to model perceived accents in different music styles. LÄS MER