In this paper, we discuss a fundamentally new approach to machine learning, called Idea Learning, and the significant improvements it offers in natural language understanding.
Current unsupervised learning systems offer tremendous promise for machine learning in terms of efficiency for implementation, but also come with significant limitations in terms of accuracy, and therefore, effectiveness for businesses. Supervised learning requires training data, and therefore, creates additional costs of time and money, and ultimately are only as good as the training data.
We explain the background of Idea Learning, share how it works differently, and the improvement in accuracy vs. time/effort that comes as a result.
To read more complete the form and download our Idea Learning Defined white paper.