Question

Why might a meta-algorithmic (or hybrid) approach to storing signals or images have a particularly high percent improvement (please provide an example)

Answer 1

Meta-algorithmics -It is proposed by Mark Twain.

The meta-algorithms are the pattern-driven means of combining two or more algorithms, classification engines. They   are powerful tools for any data scientist or architect of intelligent systems.

Generally meta-algorithmics are called intelligence generators .

The Meta-algorithmic generators are designed to provide the means of combining two or more sources of knowledge generation even when, or especially when, the combined generators are known only at the level of black box (input and output only).

There are different type of Meta Algorithms.

1- First-Order Meta-algorithmics

2- Second-Order Meta-algorithmics .

3- Third-Order Meta-algorithmics

2-Second-Order Meta-algorithmics ->

Example of the output probabilities matrix (OPM) used for the decision in the “Confusion Matrix” second-order meta-algorithmic pattern, operating on a single sample (which actually is from class C). While Classifiers 1 and 4 identify class B as the correct class, Classifiers 2 and 3 identify class A as the correct class. Interestingly, the combination of the four classifiers identifies Class C as the correct class (the sum for class C is the highest, at 1.52) -

Table-A

Output Probabilities Matrix	Classifier
	1	2	3	4	SUM
Classifier confidence (usually probability) output for class	A	0.08	0.48	0.44	0.11	1.11
B	0.51	0.13	0.24	0.49	1.37
C	.41	0.39	0.32	0.40	1.52

The output of the individual sample OPM (specifically, the “sum” column, furthest right in Table A) can be used as one input in the generation of a full confusion matrix. Since the correct class is class C.

The weights are then multiplied by the elements in the OPM to produce the weighted output probabilities matrix (WOPM)