Sökning: "Email Classification"

Visar resultat 1 - 5 av 19 uppsatser innehållade orden Email Classification.

  1. 1. 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

  3. 2. Anestesi och analgesi av akut sjuka hundar i Sverige – Rutiner och tillvägagångssätt på svenska smådjurskliniker

    Master-uppsats, SLU/Dept. of Clinical Sciences

    Författare :Kristin Svensson; [2023]
    Nyckelord :anesthesia; anesthetic protocol; analgesia; ASA; NSAID; pyometra; dogs;

    Sammanfattning : Anestesi av akut sjuka hundar innebär i regel olika anpassningar, vilka till stor del sker genom valet av anestesi- och analgesiläkemedel. De flesta typer av anestesiläkemedel har negativa kardiovaskulära effekter, medan NSAID-preparat ibland kan påverka bland annat njurarna och magtarmkanalen. LÄS MER

  4. 3. Email classification using machine learning algorithms

    Kandidat-uppsats, Uppsala universitet/Institutionen för materialvetenskap

    Författare :Isak Jonsson; [2022]
    Nyckelord :Machine learning; artificial neural networks; email classification;

    Sammanfattning : The goal of this project is to construct a machine learning algorithmthat improves over time. This was done by first constructing a datasetthat reflects real world messages, that would simulate receiving emailsfrom two different sources. The data set was constructed by combiningdata from two different online forums. LÄS MER

  5. 4. Using Semi-Supervised Learning for Email Classification

    Master-uppsats, KTH/Matematik (Avd.)

    Författare :Anders Inde; [2022]
    Nyckelord :applied mathematics; semi-supervised learning; self-training; doc2vec; classification; tillämpad matematik; semi-vägledd inlärning; self-training; doc2vec; klassificering;

    Sammanfattning : In this thesis, we investigate the use of self-training, a semi-supervised learning method, to improve binary classification of text documents. This means making use of unlabeled samples, since labeled samples can be expensive to generate. More specifically, we want to classify emails that are retrieved by Skandinaviska Enskilda Banken (SEB). LÄS MER

  7. 5. Classification of sequence tags from tandem mass spectrometry spectra using machine learning models

    Master-uppsats, Lunds universitet/Examensarbeten i bioinformatik

    Författare :Júlia Ortís Sunyer; [2022]
    Nyckelord :Biology and Life Sciences;

    Sammanfattning : Motivation: Proteomics is the large-scale study of all the proteins found in a cell, tissue or organism. In the last few years, and thanks to the development of mass spectrometry and bioinformatics, proteomics has led the research in several fields, ranging from medicine to agriculture. LÄS MER