Sökning: "machine learning"

Visar resultat 16 - 20 av 931 avhandlingar innehållade orden machine learning.

1. 16. Machine Learning for Wireless Link Adaptation : Supervised and Reinforcement Learning Theory and Algorithms

   Författare :Vidit Saxena; Joakim Jaldén; Mats Bengtsson; Hugo Tullberg; Jakob Hoydis; KTH; []
   Nyckelord :TEKNIK OCH TEKNOLOGIER; ENGINEERING AND TECHNOLOGY; TEKNIK OCH TEKNOLOGIER; ENGINEERING AND TECHNOLOGY; Wireless Communications; Reinforcement Learning; Multi-Armed Bandits; Thompson Sampling; Convex Optimization; Deep Learning; Electrical Engineering; Elektro- och systemteknik;

   Sammanfattning : Wireless data communication is a complex phenomenon. Wireless links encounter random, time-varying, channel effects that are challenging to predict and compensate. Hence, to optimally utilize the channel, wireless links adapt the data transmission parameters in real time. LÄS MER

3. 17. Towards Robust and Adaptive Machine Learning : A Fresh Perspective on Evaluation and Adaptation Methodologies in Non-Stationary Environments

   Författare :Firas Bayram; Bestoun S. Ahmed; Patrick Glauner; Karlstads universitet; []
   Nyckelord :NATURVETENSKAP; NATURAL SCIENCES; machine learning; concept drift; covariate shift; performance robustness; evaluation methodology; adaptive learning; Computer Science; Datavetenskap;

   Sammanfattning : Machine learning (ML) has become ubiquitous in various disciplines and applications, serving as a powerful tool for developing predictive models to analyze diverse variables of interest. With the advent of the digital era, the proliferation of data has presented numerous opportunities for growth and expansion across various domains. LÄS MER

4. 18. Synthetic data for visual machine learning : A data-centric approach

   Författare :Apostolia Tsirikoglou; Jonas Unger; Gabriel Eilertsen; Anders Ynnerman; Philipp Slusallek; Linköpings universitet; []
   Nyckelord :TEKNIK OCH TEKNOLOGIER; ENGINEERING AND TECHNOLOGY; Training data; Synthetic images; Computer graphics; Generative modeling; Natural images; Histopathology; Digital pathology; Machine learning; Deep learning;

   Sammanfattning : Deep learning allows computers to learn from observations, or else training data. Successful application development requires skills in neural network design, adequate computational resources, and a training data distribution that covers the application do-main. LÄS MER

5. 19. Using Learning Analytics to Understand and Support Collaborative Learning

   Författare :Mohammed Saqr; Uno Fors; Jalal Nouri; Barbara Wasson; Stockholms universitet; []
   Nyckelord :NATURVETENSKAP; NATURAL SCIENCES; Learning analytics; Social Network Analysis; Collaborative Learning; Medical Education; Interaction Analysis; Machine Learning; Information Society; informationssamhället;

   Sammanfattning : Learning analytics (LA) is a rapidly evolving research discipline that uses insights generated from data analysis to support learners and optimize both the learning process and learning environment. LA is driven by the availability of massive data records regarding learners, the revolutionary development of big data methods, cheaper and faster hardware, and the successful implementation of analytics in other domains. LÄS MER

7. 20. Machine learning for quantum information and computing

   Författare :Shahnawaz Ahmed; Chalmers tekniska högskola; []
   Nyckelord :NATURVETENSKAP; NATURAL SCIENCES; NATURVETENSKAP; NATURAL SCIENCES; NATURVETENSKAP; NATURAL SCIENCES; quantum machine learning; quantum process tomography; quantum information; Machine learning; generative neural networks; variational quantum algorithms; quantum state tomography; optimization; quantum computing; Bayesian estimation;

   Sammanfattning : This compilation thesis explores the merger of machine learning, quantum information, and computing. Inspired by the successes of neural networks and gradient-based learning, the thesis explores how such ideas can be adapted to tackle complex problems that arise during the modeling and control of quantum systems, such as quantum tomography with noisy data or optimizing quantum operations, by incorporating physics-based constraints. LÄS MER