Sökning: "optimal stopping"
Visar resultat 1 - 5 av 22 uppsatser innehållade orden optimal stopping.
1. Comparing machine learning algorithms for detecting behavioural anomalies
Uppsats för yrkesexamina på avancerad nivå, Blekinge Tekniska Högskola/Institutionen för datavetenskapSammanfattning : Background. Attempted intrusions at companies, either from an insider threat orotherwise, is increasing in frequency. Most commonly used is static analysis and filters to stop specific attacks. LÄS MER
2. Pricing and Hedging American-Style Options withDeep Learning: Algorithmic implementation
Master-uppsats, Uppsala universitet/Analys och partiella differentialekvationerSammanfattning : This thesis aims at evaluating and implementing Longstaff & Schwarz approach for approximating the value of American options. American options are generally hard to value, exercised at any time up to its expiration and moreover, there is no closed- form solution for an American option’s price. LÄS MER
3. Real-time Optimal Braking for Marine Vessels with Rotating Thrusters
Master-uppsats, KTH/Matematik (Avd.)Sammanfattning : Collision avoidance is an essential component of autonomous shipping. As ships begin to advance towards autonomy, developing an advisory system is one of the first steps. An advisory system with a strong collision avoidance component can help the crew act more quickly and accurately in dangerous situations. LÄS MER
4. Self-Play Reinforcement Learning for Finding Intrusion Prevention Strategies
Master-uppsats, KTH/Matematisk statistikSammanfattning : This Master thesis studies automated intrusion prevention using self-play reinforcement learning. We extend a decision-theoretic model of the intrusion prevention use case based on optimal stopping theory proposed in previous work to a game-theoretic setting. LÄS MER
5. Combined Regularisation Techniques for Artificial Neural Networks
Kandidat-uppsats, Lunds universitet/Beräkningsbiologi och biologisk fysik - Genomgår omorganisationSammanfattning : Artificial neural networks are prone to overfitting – the process of learning details specific to a particular training data set. Success in preventing overfitting through combining the L2 and dropout regularisation techniques has led to the combination’s recent popularity. LÄS MER