Decision Jungle - Revision history

BPeat at 02:53, 6 March 2024

2024-03-06T02:53:43Z

BPeat at 03:54, 2 March 2024

2024-03-02T03:54:06Z

BPeat at 00:53, 14 July 2023

2023-07-14T00:53:48Z

BPeat at 01:48, 6 July 2023

2023-07-06T01:48:48Z

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

2023-03-28T13:03:11Z

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

BPeat at 23:00, 22 May 2019

2019-05-22T23:00:36Z

BPeat at 04:12, 3 February 2019

2019-02-03T04:12:54Z

BPeat at 00:52, 4 June 2018

2018-06-04T00:52:47Z

BPeat: Created page with "[http://www.youtube.com/results?search_query=+Linear+Regression+artificial+intelligence YouTube search...] * AI Solver ** ...predict categories * Capabilities *..."

2018-06-03T01:20:12Z

Created page with "[http://www.youtube.com/results?search_query=+Linear+Regression+artificial+intelligence YouTube search...] * AI Solver ** ...predict categories * Capabilities *..."

New page

[http://www.youtube.com/results?search_query=+Linear+Regression+artificial+intelligence YouTube search...]

* [[AI Solver]]
** [[...predict categories]]
* [[Capabilities]]
* [http://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/linear-regression Linear Regression | Microsoft]

Linear regression is a common statistical method, which has been adopted in machine learning and enhanced with many new methods for fitting the line and measuring error. In the most basic sense, regression refers to prediction of a numeric target. Linear regression is still a good choice when you want a very simple model for a basic predictive task. Linear regression also tends to work well on high-dimensional, sparse data sets lacking complexity. Azure Machine Learning Studio supports a variety of regression models, in addition to linear regression. However, the term "regression" can be interpreted loosely, and some types of regression provided in other tools are not supported in Studio.

https://cdn-images-1.medium.com/max/640/1*eeIvlwkMNG1wSmj3FR6M2g.gif

<youtube>CtKeHnfK5uA</youtube>
<youtube>uwwWVAgJBcM</youtube>

@@ Line 17: / Line 17: @@
 [https://www.google.com/search?q=Decision+Jungle+deep+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]]
 ** [[...predict categories]]
 * [[Feature Exploration/Learning]]

@@ Line 21: / Line 21: @@
 * [[Feature Exploration/Learning]]
-Randomized decision trees and forests have a rich history in machine learning and have seen considerable success in application, perhaps particularly so for computer vision. However, they face a fundamental limitation: given enough data, the number of nodes in decision trees will grow exponentially with depth. For certain applications, for example on mobile or embedded processors, memory is a limited resource, and so the exponential growth of trees limits their depth, and thus their potential accuracy. Decision jungles, revisiting the idea of ensembles of rooted decision directed acyclic graphs (DAGs), and shows these to be compact and powerful discriminative models for classification. Unlike conventional decision trees that only allow one path to every node, a DAG in a decision jungle allows multiple paths from the root to each leaf. We present and compare two new node merging algorithms that jointly optimize both the features and the structure of the DAGs efficiently. During training, node splitting and node merging are driven by the minimization of exactly the same objective function, here the weighted sum of entropies at the leaves. Results on varied datasets show that, compared to decision forests and several other baselines, decision jungles require dramatically less memory while considerably improving generalization. [https://www.microsoft.com/en-us/research/publication/decision-jungles-compact-and-rich-models-for-classification/ Decision Jungles: Compact and Rich Models for Classification | Microsoft]
+Randomized decision trees and forests have a rich history in machine learning and have seen considerable success in application, perhaps particularly so for computer vision. However, they face a fundamental limitation: given enough data, the number of nodes in decision trees will grow exponentially with depth. For certain applications, for example on mobile or embedded processors, [[memory]] is a limited resource, and so the exponential growth of trees limits their depth, and thus their potential accuracy. Decision jungles, revisiting the idea of ensembles of rooted decision directed acyclic graphs (DAGs), and shows these to be compact and powerful discriminative models for classification. Unlike conventional decision trees that only allow one path to every node, a DAG in a decision jungle allows multiple paths from the root to each leaf. We present and compare two new node merging algorithms that jointly optimize both the features and the structure of the DAGs efficiently. During training, node splitting and node merging are driven by the minimization of exactly the same objective function, here the weighted sum of entropies at the leaves. Results on varied datasets show that, compared to decision forests and several other baselines, decision jungles require dramatically less [[memory]] while considerably improving generalization. [https://www.microsoft.com/en-us/research/publication/decision-jungles-compact-and-rich-models-for-classification/ Decision Jungles: Compact and Rich Models for Classification | Microsoft]
 <youtube>q7BwufUxteE</youtube>

@@ Line 17: / Line 17: @@
 [https://www.google.com/search?q=Decision+Jungle+deep+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]]
 ** [[...predict categories]]
 * [[Feature Exploration/Learning]]

@@ 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=Decision+Jungle+artificial+intelligence YouTube search...]
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 * [[AI Solver]]
 ** [[...predict categories]]
 * [[Feature Exploration/Learning]]

@@ 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=Decision+Jungle+artificial+intelligence YouTube search...]
+[https://www.youtube.com/results?search_query=Decision+Jungle+artificial+intelligence YouTube search...]
-[http://www.google.com/search?q=Decision+Jungle+deep+machine+learning+ML+artificial+intelligence ...Google search]
+[https://www.google.com/search?q=Decision+Jungle+deep+machine+learning+ML+artificial+intelligence ...Google search]
 * [[AI Solver]]
@@ Line 13: / Line 13: @@
 * [[Feature Exploration/Learning]]
-Randomized decision trees and forests have a rich history in machine learning and have seen considerable success in application, perhaps particularly so for computer vision. However, they face a fundamental limitation: given enough data, the number of nodes in decision trees will grow exponentially with depth. For certain applications, for example on mobile or embedded processors, memory is a limited resource, and so the exponential growth of trees limits their depth, and thus their potential accuracy. Decision jungles, revisiting the idea of ensembles of rooted decision directed acyclic graphs (DAGs), and shows these to be compact and powerful discriminative models for classification. Unlike conventional decision trees that only allow one path to every node, a DAG in a decision jungle allows multiple paths from the root to each leaf. We present and compare two new node merging algorithms that jointly optimize both the features and the structure of the DAGs efficiently. During training, node splitting and node merging are driven by the minimization of exactly the same objective function, here the weighted sum of entropies at the leaves. Results on varied datasets show that, compared to decision forests and several other baselines, decision jungles require dramatically less memory while considerably improving generalization. [http://www.microsoft.com/en-us/research/publication/decision-jungles-compact-and-rich-models-for-classification/ Decision Jungles: Compact and Rich Models for Classification | Microsoft]
+Randomized decision trees and forests have a rich history in machine learning and have seen considerable success in application, perhaps particularly so for computer vision. However, they face a fundamental limitation: given enough data, the number of nodes in decision trees will grow exponentially with depth. For certain applications, for example on mobile or embedded processors, memory is a limited resource, and so the exponential growth of trees limits their depth, and thus their potential accuracy. Decision jungles, revisiting the idea of ensembles of rooted decision directed acyclic graphs (DAGs), and shows these to be compact and powerful discriminative models for classification. Unlike conventional decision trees that only allow one path to every node, a DAG in a decision jungle allows multiple paths from the root to each leaf. We present and compare two new node merging algorithms that jointly optimize both the features and the structure of the DAGs efficiently. During training, node splitting and node merging are driven by the minimization of exactly the same objective function, here the weighted sum of entropies at the leaves. Results on varied datasets show that, compared to decision forests and several other baselines, decision jungles require dramatically less memory while considerably improving generalization. [https://www.microsoft.com/en-us/research/publication/decision-jungles-compact-and-rich-models-for-classification/ Decision Jungles: Compact and Rich Models for Classification | Microsoft]
 <youtube>q7BwufUxteE</youtube>
@@ Line 19: / Line 19: @@
 == Two-Class Decision Jungle ==
-* [http://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/linear-regression Linear Regression | Microsoft]
+* [https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/linear-regression Linear Regression | Microsoft]
 <youtube>ErVC1Tuj4IQ</youtube>

← Older revision		Revision as of 04:12, 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=Decision+Jungle+artificial+intelligence YouTube search...]		[http://www.youtube.com/results?search_query=Decision+Jungle+artificial+intelligence YouTube search...]
		+	[http://www.google.com/search?q=Decision+Jungle+deep+machine+learning+ML+artificial+intelligence ...Google search]

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