Hybridized Machine Learning

A Bicentennial Update

Machine Learning, The Basics

The conventional wisdom regarding, what could be coequally be called "learning" is that "somehow" Von Neumann derived logic systems cannot learn in an non-linear environment. This assumption is true on the most primitive primitive assumption, although the problem itself is best represented through the intricate art of statistics. To regard machine learning as the future of both computational interaction is an understatement, and as advancements hit the general consumption quo, this next age of both technological and humanistic understanding will arrive.

"Traditional purely static outlier-driven statistical learning has proven successful, artificial neural networks may be the key to grasping data outside the bayesian curve"

Artificial Neural Networks

In general ,the fluid analyzation of well defined and semi-linear data is challenging to the largely limited and direct-point drive methodology of a purely statistical approach. Recent machine modeling has taken direct inspiration from our biology and the well developing field of neural analytics has driven the uptick of such processes. In the artificial neural network, a set of vectors (generally binary) act as neurons and interact through mutual binary processes.

"Although artifical neural networks are extremely good at processing 'dynamic less linear data', inversely they are classicly bad at solving traditional statistical enumerations"

A Hybrid Approach Utilizing The Best Of Both Worlds

While both artificial neural networks and traditional statistical pattern analysis do well in their own rights, for the combined processing of non-linear pattern derived data neither suffice. This problem presents itself as data such as sound with multi-variances is quite linear in its own right. Although these factors make the data particularly useful to be processed by traditional statistical analysis, such attribute also contributes to too much variability in the output data for linear analysis to suffice. Artificial neural networks handle such improved data quite well and can build differentiating "state-farms" which can handle such tasks quite well. Overall the "hybridized" mechanism presents a promising future in dynamic machine learning