Titlebook: Artificial Intelligence and Visualization: Advancing Visual Knowledge Discovery; Boris Kovalerchuk,Kawa Nazemi,Ebad Banissi Book 2024 The

補(bǔ)充

HAIL

繁重

Explainable Machine Learning for Categorical and Mixed Data with Lossless Visualizationdesigned for numeric data. This work focuses on developing numeric coding schemes for non-numeric attributes for ML algorithms to support accurate and explainable ML models, methods for lossless visualization of n-D non-numeric categorical data with visual rule discovery in these visualizations, and

油氈

Compass

Visual Knowledge Discovery with General Line Coordinates methods already exist, these methods are often unexplainable or perform poorly on complex data. This paper proposes Visual Knowledge Discovery approaches based on several forms of lossless General Line Coordinates. These are an expansion of the previously introduced General Line Coordinates Linear

Pillory

舔食

cortisol

Computation of?Pixel-Oriented Grid Layout for?2D Datasets Using VRGridod, we propose a novel post-processing algorithm called VRGrid which allows the arrangement of any two-dimensional data in a grid while minimizing disformation of the input data. This method can be used with popular but overlap-prone projection methods such as t-SNE or MDS to obtain overlap-free and

chapel

中古

Road Traffic Flow Prediction with Visual Analyticsprediction can help improve traffic management, reduce congestion and pollution, and increase road safety. Mitigation solutions are usually used to soften the impact of this problem in most cities. In particular, the city of Lisbon has taken measures to reduce pollution by closing areas of the city