Sökning: "Slumpmässig skog"

Visar resultat 1 - 5 av 17 uppsatser innehållade orden Slumpmässig skog.

  1. 1. Performance comparison of data mining algorithms for imbalanced and high-dimensional data

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

    Författare :Daniel Rubio Adeva; [2023]
    Nyckelord :Data science; neural network; random forest; support vector machine; imbalanced data; average precision; ROC; Datavetenskap; neuralt nätverk; slumpmässig skog; stödvektormaskin; obalanserad data; medelprecision; ROC;

    Sammanfattning : Artificial intelligence techniques, such as artificial neural networks, random forests, or support vector machines, have been used to address a variety of problems in numerous industries. However, in many cases, models have to deal with issues such as imbalanced data or high multi-dimensionality. LÄS MER

  3. 2. Reliable Detection of Water Areas in Multispectral Drone Imagery : A faster region-based CNN model for accurately identifying the location of small-scale standing water bodies

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

    Författare :Shengyao Shangguan; [2023]
    Nyckelord :Water Detection; Faster region-based convolutional neural networks; Multiple images; Convolutional neural networks; Random Forest; Vattendetektering; Snabbare regionbaserade konvolutionella neurala nätverk; Flera bilder; Konvolutionella neurala nätverk; Random Forest;

    Sammanfattning : Dengue and Zika are two arboviral viruses that affect a significant portion of the world population. The principal vector species of both viruses are Aedes aegypti and Aedes albopictus mosquitoes. They breed in very slow flowing or standing pools of water. LÄS MER

  4. 3. Evaluating Random Forest and k-Nearest Neighbour Algorithms on Real-Life Data Sets

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

    Författare :Atheer Salim; Milad Farahani; [2023]
    Nyckelord :Random Forest; k-Nearest Neighbour; Evaluation; Machine Learning; Classification; Execution Time; Slumpmässig Skog; k-Närmaste Granne; Utvärdering; Maskininlärning; Klassificiering; Exekveringstid;

    Sammanfattning : Computers can be used to classify various types of data, for example to filter email messages, detect computer viruses, detect diseases, etc. This thesis explores two classification algorithms, random forest and k-nearest neighbour, to understand how accurately and how quickly they classify data. LÄS MER

  5. 4. Procedural System for Urban Forest Generation : Image-based Natural Terrain Generation

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

    Författare :Bosen Cheng; [2022]
    Nyckelord :datauppsättningar; neurala nätverk; blickdetektering; texttaggning;

    Sammanfattning : Procedural content generation is a common way to create model resources in the computer graphics area. It is beneficial for generating large-scale models such as buildings, forests, etc. This project focus on the generation of the urban forest, which is a case often overlooked. LÄS MER

  7. 5. Property Valuation by Machine Learning and Hedonic Pricing Models : A Case study on Swedish Residential Property

    Master-uppsats, KTH/Fastigheter och byggande

    Författare :Kanha Teang; Yiran Lu; [2021]
    Nyckelord :Real estate valuation; Machine learning; Hedonic Pricing Models; Random Forest; Stockholm; Fastighetsvärderingar; Maskininlärning; hedoniska prissättningsmodeller; Random Forest; Stockholm;

    Sammanfattning : Property valuation is a critical concept for a variety of applications in the real estate market such as transactions, taxes, investments, and mortgages. However, there is little consistency in which method is the best for estimating the property value. LÄS MER