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Knowledge-Based Classification Algorithms Knowledge-Based Classification Algorithms Knowledge-based classification algorithms refer to a category of machine learning algorithms that employ pre-existing knowledge or rules to classify novel instances or data points. The algorithms in question are dependent on a knowledge base, which is a compilation of established patterns, rules, or associations among attributes, for the purpose of predicting outcomes or assigning class designations to unobserved data. In contrast to conventional classification algorithms that acquire patterns or rules directly from the training data, knowledge-based classification algorithms utilise pre-existing knowledge or domain expertise to steer the classification procedure. Pre-existing knowledge can be sourced from human experts or obtained from pre-existing resources such as databases, ontologies, or expe

 Building a ground truth image is an important task in many image classification projects, especially in medical imaging or in projects that require expertise in labeling objects. However, for many other tasks, such as classifying vegetation in an image, building a labeled image can be easy and simple with the help of unsupervised learning models.                                                                       Ground truth refers to a set of measurements or conditions that serve as a benchmark or target for a classification system or machine learning algorithm. Here are some examples of what ground truth can mean in different contexts: In object recognition, ground truth can refer to a set of labeled images that have been manually annotated by human experts. These labeled images can then be used to test and benchmark the accuracy of a machine learning model. For example, in an image classification task that involves recognizing different types of cars, ground truth may consist of