# 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.

## Raw Statistical Aggregate Data This data presents very little linear thresholds | ## Aggregate Data With Applied Statistical Points This plot displays a statistical organizational pattern with linear regression applied to fluidly map the zoned patterns. | ## Aggregate Data With Applied Statistical Points The following maps symmetric prediction patterns through the application of increased training data and better channel mapping |

## Aggregate Data With Applied Statistical Points

This plot displays a statistical organizational pattern with linear regression applied to fluidly map the zoned patterns.

## "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.

## Feed-Forward Neural Networks Feed forward neural networks are the simplest in topographic layout as they do not reprocess input data through cycles in real time, and instead rely on through-pass post-processing. | ## Recurrent Neural Networks Recurrent neural networks operate with each feed-through input being cycled back in order to make arbitrary training easier. This neural network emulates the inhibition and exhibition cycle of traditional interneurons. | ## Cellular Neural Networks Cellular neural networks are a class of their own. They are the most like biological neural networks as each neuron is not simple binary training regimen, but instead exhibits characteristics of each cell being independent with its own transfer characteristics. |

## Feed-Forward Neural Networks

Feed forward neural networks are the simplest in topographic layout as they do not reprocess input data through cycles in real time, and instead rely on through-pass post-processing.

## Recurrent Neural Networks

Recurrent neural networks operate with each feed-through input being cycled back in order to make arbitrary training easier. This neural network emulates the inhibition and exhibition cycle of traditional interneurons.

## "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

## Preprocessing Using Target Factorization In such a state, the data is fed into a pre-trained statistical enumeration algorithm, before being forwarded recursively to a feed-forward neural network. Such functions help reduce noise and make back-transformations in functions such as natural language processing less intensive | ## Natural Language Preprocessing The following flow-through diagram outlines the process of language processing using a "synders-field" combination point. This process involves both post-processing and pre-processing using static flattening methods. After such process the data is fed through a recurrent neural network this process also collects weighting data to feed back into the neural network | ## Speech Processing with Asymmetric Feedback Speech processing requires dynamic comparison functions which are best implemented using dynamic neural networks. These functions are implemented with both feed-back functions in isolated "hot" areas and feed-forward for performance oriented general areas. This does not require back-training, although supplying such data through a nX*i function is helpful |

## Preprocessing Using Target Factorization

In such a state, the data is fed into a pre-trained statistical enumeration algorithm, before being forwarded recursively to a feed-forward neural network. Such functions help reduce noise and make back-transformations in functions such as natural language processing less intensive

## Natural Language Preprocessing

The following flow-through diagram outlines the process of language processing using a "synders-field" combination point. This process involves both post-processing and pre-processing using static flattening methods. After such process the data is fed through a recurrent neural network this process also collects weighting data to feed back into the neural network

## Speech Processing with Asymmetric Feedback

Speech processing requires dynamic comparison functions which are best implemented using dynamic neural networks. These functions are implemented with both feed-back functions in isolated "hot" areas and feed-forward for performance oriented general areas. This does not require back-training, although supplying such data through a nX*i function is helpful

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