Essays about: "Klassificeringsalgoritmer"

Showing result 1 - 5 of 23 essays containing the word Klassificeringsalgoritmer.

  1. 1. Measuring the Utility of Synthetic Data : An Empirical Evaluation of Population Fidelity Measures as Indicators of Synthetic Data Utility in Classification Tasks

    University essay from Karlstads universitet/Institutionen för matematik och datavetenskap (from 2013)

    Author : Alexander Florean; [2024]
    Keywords : Synthetic Data; Machine Learning; Population Fidelity Measures; Utility Metrics; Synthetic Data Quality Evaluation; Classification Algorithms; Utility Estimation; Data Privacy; Artificial Intelligence; Experiment Framework; Model Performance Assessment; Syntetisk Data; Maskininlärning; Population Fidelity Mätvärden; Användbarhetsmätvärden; Kvalitetsutvärdering av Syntetisk Data; Klassificeringsalgoritmer; Användbarhetsutvärdering; Dataintegritet; Artificiell Intelligens; AI; Experiment Ramverk; Utvärdering av Modellprestanda;

    Abstract : In the era of data-driven decision-making and innovation, synthetic data serves as a promising tool that bridges the need for vast datasets in machine learning (ML) and the imperative necessity of data privacy. By simulating real-world data while preserving privacy, synthetic data generators have become more prevalent instruments in AI and ML development. READ MORE

  3. 2. Data Classification System Based on Combination Optimized Decision Tree : A Study on Missing Data Handling, Rough Set Reduction, and FAVC Set Integration

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

    Author : Xuechun Lu; [2023]
    Keywords : Missing data handling; Rough set reduction; FAVC Set; ID3; Saknade datahantering; Rough set reducering; FAVC Set; ID3;

    Abstract : Data classification is a novel data analysis technique that involves extracting valuable information with potential utility from databases. It has found extensive applications in various domains, including finance, insurance, government, education, transportation, and defense. READ MORE

  4. 3. Exploring the Feasibility of Replicating SPAN-Model's Required Initial Margin Calculations using Machine Learning : A Master Thesis Project for Intraday Margin Call Investigation in the Commodities Market

    University essay from Umeå universitet/Institutionen för matematik och matematisk statistik

    Author : Clara Branestam; Amanda Sandgren; [2023]
    Keywords : Machine Learning; Market Risk; Initial Margin; SPAN-model; Central Counterparty Clearing; Margin Call;

    Abstract : Machine learning is a rapidly growing field within artificial intelligence that an increasing number of individuals and corporations are beginning to utilize. In recent times, the financial sector has also started to recognize the potential of these techniques and methods. READ MORE

  5. 4. Time synchronization error detection in a radio access network

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

    Author : Moulika Madana; [2023]
    Keywords : GNSS - Global Navigation Satellite System; OAS - Over-the air-synchronization; PRTC - primary reference time clock; PTP - precision time protocol; Gauss Jordan elimination; GNN- Graph Neural Network; GNSS -Globalt navigationssatellitsystem; OAS - Över-the-air tidssynkronisering; PRTC - Primär referenstidklocka; PTP - Precisionstidprotokoll; Gauss Jordan eliminering; GNN- Graf neurala nätverk;

    Abstract : Time synchronization is a process of ensuring all the time difference between the clocks of network components(like base stations, boundary clocks, grandmasters, etc.) in the mobile network is zero or negligible. It is one of the important factors responsible for ensuring effective communication between two user-equipments in a mobile network. READ MORE

  6. 5. Evaluating Random Forest and k-Nearest Neighbour Algorithms on Real-Life Data Sets

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

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

    Abstract : 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. READ MORE