[ad_1] Introduction Ensemble learning is a machine learning approach that aims to combine multiple models to improve the overall performance of a predictive task. By leveraging the diversity of multiple models, ensemble learning can often…
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[ad_1] Introduction Concept drift detection is a crucial aspect in the field of data mining and machine learning, especially in scenarios where the data distribution evolves over time. With the increasing volume of data being…
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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 Semi-supervised learning is a machine learning method that uses both labeled and unlabeled data for training purposes. In many real-world scenarios, obtaining labeled data is expensive and time-consuming, while unlabeled data is abundant.…
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[ad_1] Introduction Multi-modal learning, a subfield of machine learning, has gained significant attention in recent years due to its ability to integrate information from multiple modalities such as text, images, and audio. Cross-modal fusion, on…
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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 Imitation learning, also known as behavioral cloning, is a machine learning technique that involves learning a policy from demonstrations provided by an expert. This approach is particularly useful in settings where designing a…
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[ad_1] Introduction In today’s rapidly changing world, the ability to continuously adapt and learn throughout one’s lifetime has become increasingly important. Lifelong learning is the process of acquiring new knowledge, skills, and abilities to stay…
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[ad_1] Introduction Zero-shot learning is a promising technique in machine learning, where the model is trained on a set of classes but is able to generalize to unseen classes at test time. This approach is…
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[ad_1] Introduction: Few-shot learning is a crucial area of research in machine learning, particularly in scenarios where the amount of available data is limited. In such cases, traditional machine learning algorithms may struggle to generalize…
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