Rough Set Theory | Indiscernibility | Set Approximation | Solved Example

#neuralnetwork #softcomputing #machinelearning #datamining
Rough Set Theory | Indiscernibility | Set Approximation | Solved Example
Rough Set Theory,Its Applications.
Basic Concepts of Rough Sets.
What is information Systems.
How to find Indiscernibility.
How to find Lower, Upper and Boundary Approximation of a Set.
Introduction:1.1 Biological neurons, McCulloch and Pitts models of neuron, Types
of activation function, Network architectures, Knowledge representation, Hebb net
1.2 Learning processes: Supervised learning, Unsupervised learning and
Reinforcement learning
1.3 Learning Rules : Hebbian Learning Rule, Perceptron Learning Rule, Delta
Learning Rule, Widrow-Hoff Learning Rule, Correlation Learning Rule, WinnerTake-All Learning Rule
1.4 Applications and scope of Neural Networks
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2
Supervised Learning Networks :
2.1 Perception Networks - continuous & discrete, Perceptron convergence theorem,
Adaline, Madaline, Method of steepest descent, - least mean square algorithm,
Linear & non-linear separable classes & Pattern classes,
2.2 Back Propagation Network,
2.3 Radial Basis Function Network.
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3
Unsupervised learning network:
3.1 Fixed weights competitive nets,
3.2 Kohonen Self-organizing Feature Maps, Learning Vector Quantization,
3.3 Adaptive Resonance Theory - 1
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4
Associative memory networks:
4.1 Introduction, Training algorithms for Pattern Association,
4.2 Auto-associative Memory Network, Hetero-associative Memory Network,
Bidirectional Associative Memory,
4.3 Discrete Hopfield Networks.
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Fuzzy Logic:
5.1 Fuzzy Sets, Fuzzy Relations and Tolerance and Equivalence
5.2 Fuzzification and Defuzzification
5.3 Fuzzy Controllers

[@mengli3348]

Simple and clear. Amazing explanation!

[@suchandradas7559]

Thanks a lot. The exact things you have covered that are needed for xms

[@lailataher44]

well done, many thanks.

[@jamalnuman]

Realy great explanation particularly the example which makes all the idea clear

[@ava_chen595]

Thank you so much sir.. tomorrow is my exam.. and this helped me a lot

[@sarasingergy2713]

Amazing! I have an exam tomorrow and this helped SO MUCH

[@vanikathir155]

This is an awesome explanation sir.Thank you

[@khushbukherwar4207]

Extremely helpful short and sort👍

[@charliebaining6132]

VERY HELPFUL

[@rishav4702]

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[@btechtutorialByNishantMittal]

Shhh.. your instructor might be watching my video too!

[@mawaryagitza529]

Your explanation is a godsent. Thank You!!

[@yi-chenchen2222]

Thank you so much

[@harshaa4104]

Thank you sir

[@murnimarbun9507]

Thank you for you explain

[@swadhindas5853]

PLEASE EXPLAIN THE REST OF CONCEPTS LIKE REDUCTS AND CORE AND ALL OTHER YOU DIDN'T DISCUSSED HERE

[@vinodtahelyani1669]

Thanks Bro, You are very aweome. 😘😘

[@chanphoomkajornviriyakul9968]

thx, nice explanation

[@ganeshpatidar5012]

bahut sahi

[@bluestars6377]

Can you give me aslide on probability in rough sets

[@huntermaverick5114]

Amazing lesson. Thanks a lot dude

[@VikasKumar-nb2pn]

awesome explanation..

[@srinathsesaya7432]

Thank you

[@vanikathir155]

Sir did you upload the next video?

[@jamalnuman]

How the rough set can be used for predictions?

[@waheedahmad3151]

Good. please make other videosvrelated to applications.

[@el_dorado7680]

can you make video on - significance of attribute and approximate Reducts ??

[@murnimarbun9507]

May You Explain about Fuzzy Grid, please

[@maryamzavareh8024]

Can you make another video on the application of RST in "R"? thanx.

[@kiritisaha]

is there any more video on Rough Set Theory?

[@vijayakumarelite]

Please upload the remaining concepts sir

[@gireejatmajhiremath6751]

When will upload next videos

[@surajgupta3263]

Bhai aage ki videos kahan gyi?

[@ilaydagurbak4936]

0.75x is perfect