[ad_1] Introduction: In the field of machine learning and pattern recognition, the measurement of similarity between data points is a crucial task with implications in various applications such as image retrieval, recommendation systems, and text…
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[ad_1] Introduction Representation learning has gained significant attention in the field of machine learning and artificial intelligence in recent years. It involves learning the most effective and meaningful representations of data for a given task,…
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[ad_1] Introduction: Hierarchical learning for multi-level representations is a critical aspect in the field of machine learning and artificial intelligence. It involves the development of algorithms and models that can learn hierarchical representations of data,…
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[ad_1] Introduction: In today’s rapidly changing world, the ability to continuously learn and adapt to new information is crucial for personal and professional growth. Incremental learning, a learning strategy that involves continuously building upon existing…
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[ad_1] Thesis Overview Title: Anomaly Detection for Identifying Outliers Introduction Anomaly detection is a critical aspect of data analysis that involves identifying outliers or irregular patterns within a dataset. The ability to detect anomalies can…
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[ad_1] Introduction: Weakly supervised learning is a subfield of machine learning that aims to train models using data with noisy or incomplete labels. This is a common scenario in many real-world applications where obtaining accurately…
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[ad_1] Introduction Inverse reinforcement learning (IRL) is a subfield of machine learning that is concerned with inferring a reward function based on observed behavior. Unlike traditional reinforcement learning, where an agent learns a policy by…
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[ad_1] Introduction 1.1 Introduction 1.2 Background of study 1.3 Problem Statement 1.4 Objective of study 1.5 Limitation of study 1.6 Scope of study 1.7 Significance of study 1.8 Structure of the Thesis 1.9 Definition of…
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[ad_1] Introduction In recent years, the volume of data generated in various fields such as healthcare, finance, and social media has exponentially increased. This massive amount of data requires efficient labeling to make it usable…
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[ad_1] Introduction Meta-learning, also known as learning to learn, is a subfield of machine learning that focuses on the design and application of algorithms that can learn how to learn. The main goal of meta-learning…
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