[ad_1] Introduction: Gaussian processes are a powerful tool in machine learning for function approximation. They offer a flexible framework for modeling complex, non-linear relationships in data, while also providing uncertainty estimates for predictions. In recent…
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[ad_1] Introduction: Particle filters are a powerful tool in the field of sequential Monte Carlo methods for estimating the state of a dynamic system based on noisy observations. These filters are widely used in a…
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[ad_1] Thesis Overview Title: Partially Observable Markov Decision Processes for Imperfect Information Introduction: Partially Observable Markov decision processes (POMDPs) are a powerful framework for modeling decision-making problems in the presence of uncertainty. In many real-world…
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[ad_1] Introduction: In today’s competitive and volatile business environment, organizations are constantly faced with uncertainty and unpredictable events that can have a significant impact on their operations and outcomes. Planning under uncertainty is a critical…
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[ad_1] Introduction Knowledge representation is a key aspect of intelligent systems, enabling them to understand and reason about the world around them. By encoding information in a form that can be easily processed by algorithms,…
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[ad_1] Introduction Document classification is the process of categorizing text documents into different predefined classes or categories. It is a fundamental task in information retrieval and natural language processing, with applications in various fields such…
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[ad_1] Introduction Keyword extraction is a crucial process in document indexing that involves identifying and extracting the most relevant terms from a document to facilitate efficient information retrieval and organization. With the exponential growth of…
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[ad_1] Introduction In recent years, the field of natural language processing (NLP) has seen significant advancements in the use of word embeddings for vector representation. Word embeddings are mathematical representations of words in a continuous…
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[ad_1] Introduction Semantic similarity is a crucial concept in the field of Natural Language Processing (NLP) that aims to measure the relatedness between words based on their meanings. The ability to accurately determine the semantic…
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[ad_1] Introduction: Constituency parsing is a fundamental task in natural language processing that involves analyzing the structure of a sentence based on its syntactic constituents. In phrase structure parsing, sentences are parsed based on the…
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