![HK Lam](/img/default-banner.jpg)
- 76
- 124 376
HK Lam
Приєднався 8 лют 2012
Particle Swarm Optimization - Part 5: Veclocity Clamping
This video is about Particle Swarm Optimization - Part 5: Veclocity Clamping
Переглядів: 337
Відео
Particle Swarm Optimization - Part 4: Velocity Components
Переглядів 278Рік тому
This video is about Particle Swarm Optimization - Part 4: Velocity Components
Particle Swarm Optimization - Part 3: Local Best PSO
Переглядів 619Рік тому
This video is about Particle Swarm Optimization - Part 3: Local Best PSO
Particle Swarm Optimization - Part 2: Global Best PSO
Переглядів 829Рік тому
This video is about Particle Swarm Optimization - Part 2: Global Best PSO
Particle Swarm Optimization (PSO) - Part 1: Introduction
Переглядів 928Рік тому
This video is about Particle Swarm Optimization (PSO) - Part 1: Introduction
Cultering for Unknown Number of CLusters - Unsupervised Learning and Clustering
Переглядів 124Рік тому
This video is about Cultering for Unknown Number of CLusters - Unsupervised Learning and Clustering
Competitive Learning - Unsupervised Learning and Clustering
Переглядів 320Рік тому
This video is about Competitive Learning - Unsupervised Learning and Clustering
Hierarchical Clustering - Unsupervised Learning and Clustering
Переглядів 75Рік тому
This video is about Hierarchical Clustering - Unsupervised Learning and Clustering
Iterative Optimisation - Unsupervised Learning and Clustering
Переглядів 301Рік тому
This video is about Iterative Optimisation - Unsupervised Learning and Clustering
Fuzzy K-Means Clustering - Unsupervised Learning and Clustering
Переглядів 3,6 тис.Рік тому
This video is about Fuzzy K-Means Clustering - Unsupervised Learning and Clustering
K-Means Clustering - Unsupervised Learning and Clustering
Переглядів 137Рік тому
This video is about K-Means Clustering - Unsupervised Learning and Clustering
Unsupervised Learning and Clustering - Introduction
Переглядів 67Рік тому
This video is about Unsupervised Learning and Clustering - Introduction
Intelligent Control and Machine Learning - Concepts and Applications from Engineering Mind by HK Lam
Переглядів 2952 роки тому
This video is about Intelligent Control and Machine Learning - Concepts and Applications from Engineering Mind by HK Lam This was a presentation given in Interdisciplinary workshop on emerging New Approach Methodologies (NAMs), held in 24th March #machinelearning #controltheory #fuzzylogic
Ant Colony Optimization - Part 6: Ant Colony Systsem (ACS)
Переглядів 3 тис.2 роки тому
This video is about Ant Colony Optimization - Part 6: Ant Colony Systsem (ACS)
Ant Colony Optimization - Part 5: Example - Traveling Saleman Problem (TSP)
Переглядів 26 тис.2 роки тому
This video is about Ant Colony Optimization - Part 5: Example - Traveling Saleman Problem (TSP)
Ant Colony Optimization - Part 4: Ant System (AS)
Переглядів 2 тис.2 роки тому
Ant Colony Optimization - Part 4: Ant System (AS)
Ant Colony Optimization - Part 3.3: Simple Ant Colony Optimization (SACO) - Detailed Exaplination
Переглядів 1,9 тис.2 роки тому
Ant Colony Optimization - Part 3.3: Simple Ant Colony Optimization (SACO) - Detailed Exaplination
Ant Colony Optimization - Part 3.2: Simple Ant Colony Optimization (SACO) - Example
Переглядів 1,6 тис.2 роки тому
Ant Colony Optimization - Part 3.2: Simple Ant Colony Optimization (SACO) - Example
Ant Colony Optimization - Part 3.1: Simple Ant Colony Optimization (SACO)
Переглядів 3,8 тис.2 роки тому
Ant Colony Optimization - Part 3.1: Simple Ant Colony Optimization (SACO)
Ant Colony Optimization - Part 2: Stigmergy and Artifical Pheromone
Переглядів 1,6 тис.2 роки тому
Ant Colony Optimization - Part 2: Stigmergy and Artifical Pheromone
Ant Colony Optimization - Part 1: Introduction
Переглядів 3 тис.2 роки тому
Ant Colony Optimization - Part 1: Introduction
Evolution Strategy - Part 5 - Crossover Operators
Переглядів 5532 роки тому
Evolution Strategy - Part 5 - Crossover Operators
Evolution Strategy (ES) - Part 4 - Selection Strategy
Переглядів 6002 роки тому
Evolution Strategy (ES) - Part 4 - Selection Strategy
Evolution Strategy (ES) - Part 3 - (μ+1)-ES
Переглядів 8282 роки тому
Evolution Strategy (ES) - Part 3 - (μ 1)-ES
Evolution Strategy (ES) - Part 2 - (1+1)-ES
Переглядів 1,3 тис.2 роки тому
Evolution Strategy (ES) - Part 2 - (1 1)-ES
Evolution Strategry (ES) - Part 1 - Introduction to Evolution Strategy
Переглядів 1,9 тис.2 роки тому
Evolution Strategry (ES) - Part 1 - Introduction to Evolution Strategy
Continuous Genetic Algorithm - Part 1
Переглядів 1,2 тис.2 роки тому
Continuous Genetic Algorithm - Part 1
Central Limit Theorem: Verification using Exponential Distribution with mu = 5
Переглядів 2062 роки тому
Central Limit Theorem: Verification using Exponential Distribution with mu = 5
Central Limit Theorem: Verification using Geometric Distribution with p = 0.8
Переглядів 1472 роки тому
Central Limit Theorem: Verification using Geometric Distribution with p = 0.8
0vO, with the same votes, can we just pick the one that is farthest from hyperplane?
Good explanation, thank you !!😀
Thank you sir
Hi, I can see this Salesmen Travelling Problem is symmetric, i.e., the distance matrix is symmetrical, the distance traveled between two points is the same. By the same way, can I proposed the pheromone travelled between two points in this problem be the same? i.e, the pheromone matrix is symmetrical in the Salesmen Travelling Problem? Will this through affect the solution?
always nice to learn a thing or two from elon musk.
Good video. Where are your sources from? So i can counter my prof the next time his ass ask where do i get my algorithm lol
Wow that is really clarifying a lot of things in one place. Very greatfull!
Thank you. 😀
Amazing video, thanks for sharing this valuable content, what are the advantages of the encoding/decoding process? Performance?
Sir please explain, if the original path chosen for each ant in the beginning is through transition probabilities and if yes how, or if it is random
Hello sir, How can we define the optimal theta ?
Many thank for six part videos. Great job!!! BUt I have a question? Could you tell me what't the different between the Part6(ACS) and Part5(ACO) video?
Based on my understanding. ACO uses the pheromones bias method which includes alpha and beta, where ants will be biased for what other ants used. Still, we can control that by having a larger alpha we favor old experience (exploitation) but by having a larger beta we bias exploration plus we have 3 types of updates -1-pheromones placement -2-pheromones evaporation (global update) -3-path quality update Q/L In ACS there is no alpha parameter to control pheromones from our end, but we have an additional type of update regarding pheromones which is a local update. The local update will be applied to every ant after visiting nodes A, B which will reduce the amount of pheromones to encourage other ants to explore another path, you can think of it as ants saying "Ay YO we tried this path, try different one" So basically we can say that in ASC pheromones evaporate or disappear 2 times each iteration (local update - global update), whereas in ACO pheromones will evaporate or disappear 1 time (global update) Hope this helps.
@yixingzhang2462
Hi , its great. how can we access to your notes?
支持
Awesome content !!! Thank you
Prof Lam, At 11:50 for the AS method: why the contributions from ant 1, 2 and 5 are 1/15? Would you please elaborate on this? Should they be the same as SACO (1/35, 1/55, 1/40)? Thank you.
Thanks for your comment. The contributions from 1, 2 and 5 are 1/15 because they follow the Ant-quantity AS contribution rule given on the bottom right-hand side of the slide at 11:50. The update rule is Q/d_{ij}(t).
Super helpful video, thanks heaps : )
I did not understand are hidden layer input weights "c" and centroids we choose "c" the same "c"?
how did you manage to put on ads, while you are only having 665 subscribers
Professor please help in Stability analysis of closed loop TS Fuzzy system. Please provide your email I'd
Dear Professor, could you please teach something about T-S fuzzy systems?😍
Thank you professor
Thank you.
@@hklam2368 Can i contact you regarding this presentation there is point i didn't understand and I'm working on a AI project? thanks
How can u check the convergence of this method
I didn't check the convergence. When applying the algorithm, if it diverges, could stop the algorithm and start from another initial position.
What is the stopping criteria of this method
The stopping criteria could be 1) the maximum number of iterations is met, 2) the solutions of the vertices are very closed to each other, 3) the best vertex in the past n iterations do not change much.
layhfdk a3chiri
best explanation of multiclass classification using SVM, thanks Prof. Lam.
Thank you.
Looks like not a lot of people study these though
Amazing resource, really!
Great video, thank you for sharing it
What is the end of this method?how do I get to know the answer?
There should be a stopping criterion, e.g., the average change of the best vertices in the past 5 iterations is within a user-specified bound.If this criterion is met, the best vertex obtained in the last iteration is taken as the answer (solution).
Hi, you have to do NES and CMA-ES, that'd be perfect.
beautifully explained, thanks.
Thank you.
Hi, awesome lectures, going through them, quick and dirty nicely explained, thanks.
Excellent video..one question though, on the mobile robot doesn't the error angle have a polarity as well as a magnitude?
Thanks for the video, well explained!
thank you sir
Your Nelder-Mead Algorithm works very well Which source (paper, books) do you use for this video? Thank you
Could you upload the slides for the module if possible please
Would it be possible for you to upload the tutorial questions and solution pdfs for this module in a Google drive folder and share the link?
This is very well done. Thank you for the clear explanation.
Thank you.
This is awesome, but I have a question, on the slide at 3:50 what does lambda[i] represent?
It is the Lagrange multiplier for the i-th sample (or support vector)
@@hklam2368 Thank You, I am taking Mchine Learning at Cal State University and the material is much less dense than yours so I was struggling to follow, but now I see! I appreciate such a high level lesson!
@@brandonteller5360 Thank you, 😀
thank you
Hi sir ,how can I download slides, because the link don't active
nms.kcl.ac.uk/hk.lam/HKLam/images/HKLam/presentations/Interval%20Type-2%20Fuzzy%20System%20and%20its%20Applications.pdf
Very helpful!
Hi from TU Kaiserslautern, Germany. Writing on my diploma thesis. Very thankful for your videos! Pretty much some of the best explanations to actually be able to write code for yourself at home in any language. Thank you!
Why we Use RBFNN for approximate unknown functions in nonlinear systems?
thank you sir for giving clear explaination
Great video. It helped a lot to implement the algorithm in the context of machine scheduling.
Thanks