Sökning: "klassifikationsmodell"

Visar resultat 1 - 5 av 9 uppsatser innehållade ordet klassifikationsmodell.

1. 1. Att med hjälp av fotbollsstatistik hitta en lämplig klassifikationsmodell som förklarar resultatet av en fotbollsmatch

   Kandidat-uppsats, KTH/Skolan för teknikvetenskap (SCI)

   Författare :Mostafa Mohammadi; [2023]
   Nyckelord :klassifikationsmodell;

   Sammanfattning : Baserat på fotbollsstatistik från Premier League under säsongen 2022–2023 utforskar vi möjligheten att hitta en pålitlig klassificeringsmodell som använder fotbollsstatistik för att förklara resultatet av en fotbollsmatch. Dessutom undersöks korrelationen mellan oberoende variabler och utfallet. LÄS MER

3. 2. An Evaluation of Classical and Quantum Kernels for Machine Learning Classifiers

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

   Författare :Teo Nordström; Jacob Westergren; [2023]
   Nyckelord :Machine Learning; Quantum Computing; Kernels; Support Vector Machines; Maskininlärning; Kvantberäkning; Kärnor; Stödvektormaskin;

   Sammanfattning : Quantum computing is an emerging field with potential applications in machine learning. This research project aimed to compare the performance of a quantum kernel to that of a classical kernel in machine learning binary classification tasks. LÄS MER

4. 3. A Comparative Study on the Effects of Removing the Most Important Feature on Random Forest and Support Vector Machine

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

   Författare :Henrik Åkesson; Hampus Fridlund; [2023]
   Nyckelord :;

   Sammanfattning : Machine learning (ML) for classification is largely regarded as a “black box”, in that it’s difficult to fully understand how the model reached a decision, and how changes to the input affects the output. Therefore, exploring the inner workings of classification models are of interest for expanding the current knowledge base, providing guidelines for choosing a more suitable classification model for a specific problem. LÄS MER

5. 4. Classification Method of Financial Behaviour Through Means of Machine Learning : Can a classification method created using bank transaction and machine learning help individuals to understand their spending behavior?

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

   Författare :Yilei Cheng; Akhmed Al-Sayed; [2022]
   Nyckelord :machine learning; bank transactions; classification; spending behaviour; overspending; money awareness.; maskininlärning; banktransaktioner; klassificering; utgiftsbeteende; överutgifter; finansiell kunskap.;

   Sammanfattning : With the current fast transformation from physical cash to digitized banking systems, there are more and more people that are at risk of overspending without realizing it. There are methods and researches done that are targeted at incorporating machine learning in identifying fraudulent transactions and credit scores but currently there is no research done in categorizing people’s behaviour based on transaction records using machine learning techniques. LÄS MER

7. 5. The Impact of Noise on Generative and Discriminative Image Classifiers

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

   Författare :Maximilian Stenlund; Valdemar Jakobsson; [2022]
   Nyckelord :Artificial intelligence; Adversarial noise; Discriminative; Generative; Salt and Pepper noise; Gaussian noise; neural networks; Normalized flows; Convolutional networks;

   Sammanfattning : This report analyzes the difference between discriminative and generative image classifiers when tested on noise. The generative classifier was a maximum-likelihood based classifier using a normalizing flow as the generative model. In this work, a coupling flow such as RealNVP was used. LÄS MER