Spark’s built-in machine-learning library (MLlib) provides a key differentiator from predecessor open source technologies and leverages Spark’s distributed, in-memory execution model. Take a look at some practical applications for specific Spark machine-learning algorithms in three advanced
Kernel-based learning methods form the foundation of an important approach to machine learning, particularly as relates to text classification. Several kernels that are especially notable for their usefulness in text mining also hold potential for other advanced analytics applications.
Text mining is the next step in data mining, offering advanced capabilities for extracting meaning from vast, amorphous masses of data. Despite its complexity, text mining has much to offer businesses—and the list is growing. Discover what text mining could mean for your organization today.
A document classification model can join together with text analytics to categorize documents dynamically, determining their value and sending them for further processing. Learn how a quick, efficient solution can create business advantage.