Visualizing Learned Models and Data for Exploratory Machine Learning

可视化学习模型和数据以进行探索性机器学习

• 批准号: 9625726
• 负责人: Armand Prieditis
• 金额: $ 15.9万
• 依托单位: University of California-Davis
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 1996
• 资助国家: 美国
• 起止时间: 1996-07-15 至 1998-11-03
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9625726&HistoricalAwards=false
• 关键词: Visualizing; Learned; Models; Data; Exploratory

Visualizing Machine Learning Models and Data Choosing an appropriate model for the data or changing an existing model are both essential, but difficult steps in machine learning. Surprisingly, the trend in machine learning research has been to ignore that wonderful visual information processor--the human--and to build learning systems that are stand-alone and fully automated. This research is attempting to show that machine learning is a task best shared by humans and machine because of their unique capabilities. Human vision gives us built-in features such as motion detection and direction, stereoscopic depth, edge and shape detection, grouping by color, light, and shade. Because of these features, humans are good at quickly recognizing complex patterns in visual data, quickly detecting outliers in visual data, and visually manipulating a model to reflect the data. In contrast, machines are good at fast, accurate, and repetitive calculations necessary for machine learning. Not only is an appropriate division of labor between human and machine important, but a visualization of a learned model can serve as a visual explanation of why the learned model fits the data. Finally, visualization together with direct manipulation of the model can make it easier for the user to change the model to reflect the data, immediately see the results, and focus on interesting data regions. The impact of this research is that is may become easier to find good learning models and to visually understand why they are good.

在机器学习中，为数据选择合适的模型或更改现有模型都是必不可少的，但也是困难的步骤。令人惊讶的是，机器学习研究的趋势是忽略了人类这个奇妙的视觉信息处理器，而构建了独立的、完全自动化的学习系统。这项研究试图表明，由于人类和机器的独特能力，机器学习是一项最好由人类和机器共同完成的任务。人类视觉为我们提供了诸如运动检测和方向、立体深度、边缘和形状检测、颜色、光线和阴影分组等内置功能。由于这些特征，人类擅长快速识别视觉数据中的复杂模式，快速检测视觉数据中的异常值，并通过视觉操作模型来反映数据。相比之下，机器擅长快速、准确和重复的计算，这是机器学习所必需的。不仅人与机器之间的适当分工很重要，而且学习模型的可视化可以作为学习模型适合数据的可视化解释。最后，可视化和对模型的直接操作可以使用户更容易地更改模型以反映数据，立即看到结果，并关注感兴趣的数据区域。这项研究的影响是，我们可能更容易找到好的学习模式，并直观地理解为什么它们是好的。