Expectation–Maximization (EM) Clustering using Gaussian Mixture Models (GMM) - Revision history

BPeat at 02:47, 6 March 2024

2024-03-06T02:47:50Z

BPeat at 14:00, 13 September 2023

2023-09-13T14:00:41Z

BPeat at 22:40, 16 August 2023

2023-08-16T22:40:36Z

BPeat at 01:18, 14 July 2023

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BPeat at 01:40, 6 July 2023

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BPeat: Text replacement - "http:" to "https:"

2023-03-28T16:52:51Z

Text replacement - "http:" to "https:"

BPeat at 00:41, 23 April 2019

2019-04-23T00:41:42Z

BPeat at 14:15, 3 February 2019

2019-02-03T14:15:38Z

BPeat at 23:08, 7 January 2019

2019-01-07T23:08:12Z

BPeat at 19:53, 2 June 2018

2018-06-02T19:53:21Z

@@ Line 17: / Line 17: @@
 [https://www.google.com/search?q=Expectation+Maximization+EM+Clustering+Gaussian+Mixture+GMM+machine+learning+ML+artificial+intelligence ...Google search]
-* [[AI Solver]] ... [[Algorithms]] ... [[Algorithm Administration|Administration]] ... [[Model Search]] ... [[Discriminative vs. Generative]] ... [[Optimizer]] ... [[Train, Validate, and Test]]
+* [[AI Solver]] ... [[Algorithms]] ... [[Algorithm Administration|Administration]] ... [[Model Search]] ... [[Discriminative vs. Generative]] ... [[Train, Validate, and Test]]
 ** [[...cluster]]
 * [[Embedding]] ... [[Fine-tuning]] ... [[Retrieval-Augmented Generation (RAG)|RAG]] ... [[Agents#AI-Powered Search|Search]] ... [[Clustering]] ... [[Recommendation]] ... [[Anomaly Detection]] ... [[Classification]] ... [[Dimensional Reduction]].  [[...find outliers]]

@@ Line 19: / Line 19: @@
 * [[AI Solver]] ... [[Algorithms]] ... [[Algorithm Administration|Administration]] ... [[Model Search]] ... [[Discriminative vs. Generative]] ... [[Optimizer]] ... [[Train, Validate, and Test]]
 ** [[...cluster]]
-* [[Embedding]] ... [[Fine-tuning]] ... [[Agents#AI-Powered Search|Search]] ... [[Clustering]] ... [[Recommendation]] ... [[Anomaly Detection]] ... [[Classification]] ... [[Dimensional Reduction]].  [[...find outliers]]
+* [[Embedding]] ... [[Fine-tuning]] ... [[Retrieval-Augmented Generation (RAG)|RAG]] ... [[Agents#AI-Powered Search|Search]] ... [[Clustering]] ... [[Recommendation]] ... [[Anomaly Detection]] ... [[Classification]] ... [[Dimensional Reduction]].  [[...find outliers]]
 One of the major drawbacks of K-Means is its naive use of the mean value for the cluster center. We can see why this isn’t the best way of doing things by looking at the image below. On the left hand side it looks quite obvious to the human eye that there are two circular clusters with different radius’ centered at the same mean. K-Means can’t handle this because the mean values of the clusters are a very close together. K-Means also fails in cases where the clusters are not circular, again as a result of using the mean as cluster center. [https://towardsdatascience.com/the-5-clustering-algorithms-data-scientists-need-to-know-a36d136ef68 The 5 Clustering Algorithms Data Scientists Need to Know | Towards Data Science]

@@ Line 19: / Line 19: @@
 * [[AI Solver]] ... [[Algorithms]] ... [[Algorithm Administration|Administration]] ... [[Model Search]] ... [[Discriminative vs. Generative]] ... [[Optimizer]] ... [[Train, Validate, and Test]]
 ** [[...cluster]]
-* [[Embedding]]:  [[Agents#AI-Powered Search|Search]] ... [[Clustering]] ... [[Recommendation]] ... [[Anomaly Detection]] ... [[Classification]] ... [[Dimensional Reduction]] ... [[...find outliers]]
+* [[Embedding]] ... [[Fine-tuning]] ... [[Agents#AI-Powered Search|Search]] ... [[Clustering]] ... [[Recommendation]] ... [[Anomaly Detection]] ... [[Classification]] ... [[Dimensional Reduction]].  [[...find outliers]]
 One of the major drawbacks of K-Means is its naive use of the mean value for the cluster center. We can see why this isn’t the best way of doing things by looking at the image below. On the left hand side it looks quite obvious to the human eye that there are two circular clusters with different radius’ centered at the same mean. K-Means can’t handle this because the mean values of the clusters are a very close together. K-Means also fails in cases where the clusters are not circular, again as a result of using the mean as cluster center. [https://towardsdatascience.com/the-5-clustering-algorithms-data-scientists-need-to-know-a36d136ef68 The 5 Clustering Algorithms Data Scientists Need to Know | Towards Data Science]

@@ Line 17: / Line 17: @@
 [https://www.google.com/search?q=Expectation+Maximization+EM+Clustering+Gaussian+Mixture+GMM+machine+learning+ML+artificial+intelligence ...Google search]
-* [[AI Solver]]
+* [[AI Solver]] ... [[Algorithms]] ... [[Algorithm Administration|Administration]] ... [[Model Search]] ... [[Discriminative vs. Generative]] ... [[Optimizer]] ... [[Train, Validate, and Test]]
 ** [[...cluster]]
-* [[Clustering]]
+* [[Embedding]]:  [[Agents#AI-Powered Search|Search]] ... [[Clustering]] ... [[Recommendation]] ... [[Anomaly Detection]] ... [[Classification]] ... [[Dimensional Reduction]] ... [[...find outliers]]
 One of the major drawbacks of K-Means is its naive use of the mean value for the cluster center. We can see why this isn’t the best way of doing things by looking at the image below. On the left hand side it looks quite obvious to the human eye that there are two circular clusters with different radius’ centered at the same mean. K-Means can’t handle this because the mean values of the clusters are a very close together. K-Means also fails in cases where the clusters are not circular, again as a result of using the mean as cluster center. [https://towardsdatascience.com/the-5-clustering-algorithms-data-scientists-need-to-know-a36d136ef68 The 5 Clustering Algorithms Data Scientists Need to Know | Towards Data Science]

@@ Line 2: / Line 2: @@
 |title=PRIMO.ai
 |titlemode=append
-|keywords=artificial, intelligence, machine, learning, models, algorithms, data, singularity, moonshot, Tensorflow, Google, Nvidia, Microsoft, Azure, Amazon, AWS
+|keywords=ChatGPT, artificial, intelligence, machine, learning, GPT-4, GPT-5, NLP, NLG, NLC, NLU, models, data, singularity, moonshot, Sentience, AGI, Emergence, Moonshot, Explainable, TensorFlow, Google, Nvidia, Microsoft, Azure, Amazon, AWS, Hugging Face, OpenAI, Tensorflow, OpenAI, Google, Nvidia, Microsoft, Azure, Amazon, AWS, Meta, LLM, metaverse, assistants, agents, digital twin, IoT, Transhumanism, Immersive Reality, Generative AI, Conversational AI, Perplexity, Bing, You, Bard, Ernie, prompt Engineering LangChain, Video/Image, Vision, End-to-End Speech, Synthesize Speech, Speech Recognition, Stanford, MIT |description=Helpful resources for your journey with artificial intelligence; videos, articles, techniques, courses, profiles, and tools
 |description=Helpful resources for your journey with artificial intelligence; videos, articles, techniques, courses, profiles, and tools
 }}
 [https://www.youtube.com/results?search_query=Expectation+Maximization+EM+Clustering+Gaussian+Mixture+GMM Youtube search...]
@@ Line 10: / Line 19: @@
 * [[AI Solver]]
 ** [[...cluster]]
 * [[Clustering]]

@@ Line 5: / Line 5: @@
 |description=Helpful resources for your journey with artificial intelligence; videos, articles, techniques, courses, profiles, and tools
 }}
-[http://www.youtube.com/results?search_query=Expectation+Maximization+EM+Clustering+Gaussian+Mixture+GMM Youtube search...]
+[https://www.youtube.com/results?search_query=Expectation+Maximization+EM+Clustering+Gaussian+Mixture+GMM Youtube search...]
-[http://www.google.com/search?q=Expectation+Maximization+EM+Clustering+Gaussian+Mixture+GMM+machine+learning+ML+artificial+intelligence ...Google search]
+[https://www.google.com/search?q=Expectation+Maximization+EM+Clustering+Gaussian+Mixture+GMM+machine+learning+ML+artificial+intelligence ...Google search]
 * [[AI Solver]]
@@ Line 13: / Line 13: @@
 * [[Clustering]]
-One of the major drawbacks of K-Means is its naive use of the mean value for the cluster center. We can see why this isn’t the best way of doing things by looking at the image below. On the left hand side it looks quite obvious to the human eye that there are two circular clusters with different radius’ centered at the same mean. K-Means can’t handle this because the mean values of the clusters are a very close together. K-Means also fails in cases where the clusters are not circular, again as a result of using the mean as cluster center. [http://towardsdatascience.com/the-5-clustering-algorithms-data-scientists-need-to-know-a36d136ef68 The 5 Clustering Algorithms Data Scientists Need to Know | Towards Data Science]
+One of the major drawbacks of K-Means is its naive use of the mean value for the cluster center. We can see why this isn’t the best way of doing things by looking at the image below. On the left hand side it looks quite obvious to the human eye that there are two circular clusters with different radius’ centered at the same mean. K-Means can’t handle this because the mean values of the clusters are a very close together. K-Means also fails in cases where the clusters are not circular, again as a result of using the mean as cluster center. [https://towardsdatascience.com/the-5-clustering-algorithms-data-scientists-need-to-know-a36d136ef68 The 5 Clustering Algorithms Data Scientists Need to Know | Towards Data Science]
 https://cdn-images-1.medium.com/max/640/1*Xvl-pXxsLAZ7gbTUuvgMtA.png
@@ Line 31: / Line 31: @@
 == Expectation–Maximization (EM)  ==
-[http://www.youtube.com/results?search_query=Expectation+Maximization+EM+Clustering Youtube search...]
+[https://www.youtube.com/results?search_query=Expectation+Maximization+EM+Clustering Youtube search...]
 <youtube>XLKoTqGao7U</youtube>

← Older revision		Revision as of 14:15, 3 February 2019
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		+	{{#seo:
		+	\|title=PRIMO.ai
		+	\|titlemode=append
		+	\|keywords=artificial, intelligence, machine, learning, models, algorithms, data, singularity, moonshot, Tensorflow, Google, Nvidia, Microsoft, Azure, Amazon, AWS
		+	\|description=Helpful resources for your journey with artificial intelligence; videos, articles, techniques, courses, profiles, and tools
		+	}}
	[http://www.youtube.com/results?search_query=Expectation+Maximization+EM+Clustering+Gaussian+Mixture+GMM Youtube search...]		[http://www.youtube.com/results?search_query=Expectation+Maximization+EM+Clustering+Gaussian+Mixture+GMM Youtube search...]
		+	[http://www.google.com/search?q=Expectation+Maximization+EM+Clustering+Gaussian+Mixture+GMM+machine+learning+ML+artificial+intelligence ...Google search]

	* [[AI Solver]]		* [[AI Solver]]