[ad_1] **Introduction** Gradient boosting machines (GBM) have become a popular machine learning technique for building predictive models in various fields such as finance, healthcare, and marketing. GBM is a powerful ensemble learning method that combines…
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[ad_1] Introduction: Decision trees are a popular and widely used machine learning technique that provides an interpretable model for making decisions. This thesis explores the use of decision trees for developing interpretable models, focusing on…
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[ad_1] Introduction Hidden Markov models (HMMs) are powerful statistical models used in a wide range of applications, including speech recognition, bioinformatics, and natural language processing. In recent years, they have become increasingly popular for sequence…
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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 Document clustering is the process of grouping similar documents together based on their content or attributes. This technique is widely used in various applications such as information retrieval, text mining, and document organization.…
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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: Word sense disambiguation (WSD) is a crucial task in the field of lexical semantics, with the objective of determining the correct sense of a word within a given context. The ambiguity of natural…
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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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[ad_1] Introduction Semantic Role Labeling (SRL) is a crucial task in natural language processing that involves identifying the semantic roles of words in a sentence and assigning them to their corresponding predicates. The goal of…
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