Top Tweets for #Ensemblelearning
Our GA-optimized Ensemble model achieved an average 91.61% accuracy in predicting $SKY daily closing prices over the latest 3-day evaluation period.
The results demonstrate the effectiveness of ensemble learning combined with evolutionary optimization.
#SKY #EnsembleLearning #GeneticAlgorithm #AI #MachineLearning #Crypto #Forecasting
๐ TON Prediction Summary
Average accuracy: 95.67% ๐
Model: ACO + Ensemble
Period: 2026-06-13 โ 2026-06-16
Consistent forecasting performance across volatile price action.
#TON #Crypto #AITrading #AntColonyOptimization #EnsembleLearning #MachineLearning
๐ข #highlycited paper
๐ #EnsembleLearning-Based #WeedDetection from a Duckโs Perspective Using an #AquaticDrone in #RicePaddiesย โ
๐ https://t.co/mDn5GFYGg6
๐จโ๐ฌ by Soma Asuka et al.
๐ซ Tokyo University of Agriculture and Technology
#semanticsegmentation #imagedataset

๐ $OKB Price Prediction Update:
Our SA-optimized Ensemble model hit a high precision mark, achieving an average accuracy of 98.37% for recent daily close prices.
Reliable forecasting for the OKX ecosystem! ๐ฆ๐
#OKB #OKX #CryptoPredictions #EnsembleLearning #AI
๐ฏย #LLMs are powerful but imperfect. #EnsembleLearning combines multiple models to fix bias.
๐คRead #HighlyCitedArticle "Ensemble Large Language Models: A Survey" by Ibomoiye Domor Mienye and Theo G. Swart.
See more details at:
https://t.co/HOkJv4GLVS
#NLP #AI
@ComSciMath_Mdpi

Day 68 of ML:
Learned AdaBoost step-by-step . [Part-1]
from decision stumps to weight updates and upsampling. Weak learners combining into a strong model is powerful.
github : [https://t.co/Y1CwCfAcZX]
#Day68 #100DaysOfML #BuildInPublic #AdaBoost #Boosting #EnsembleLearning
day 66 of ML !!
Single model: overfits
Bagging: stabilizes
Todayโs lesson โ multiple weak models > one strong model.
checkout the code : [https://t.co/gFeP3MzKBp]
#MachineLearning #DataScience #AI #EnsembleLearning #MLJourney #BuildInPublic
![TheRealDiwanshu's tweet photo. day 66 of ML !!
Single model: overfits
Bagging: stabilizes
Todayโs lesson โ multiple weak models > one strong model.
checkout the code : [https://t.co/gFeP3MzKBp]
#MachineLearning #DataScience #AI #EnsembleLearning #MLJourney #BuildInPublic https://t.co/OAa5Sx2ysC](https://pbs.twimg.com/media/HFFxKKTbYAAkZmH.png)
Providing a better understanding of the dynamics of the COVID-19 #pandemic and for informed decision-making under uncertainty: โA novel #ensemblelearning technique of #shallowmodels applied on a COVID-19 datasetโ by Diogen Babuc. ACSIS Vol. 39 p. 537โ542; https://t.co/Lt4f66w33i

Day 78 - #DataScience Journey :
-> Today, I started Ensemble Learning! ๐
-> The genius idea: combining "weak learners" to create one super-stable model. ๐ง
-> Focused on Bagging (like Random Forest) and the power of Boosting (AdaBoost, XGBoost, etc.).
#ML #EnsembleLearning
Top Books on #EnsembleLearning! #BigData #Analytics #DataScience #AI #MachineLearning #IoT #IIoT #Python #RStats #TensorFlow #Java #JavaScript #ReactJS #GoLang #CloudComputing #Serverless #DataScientist #Linux #Books #Programming #Coding #100DaysofCode
https://t.co/BdYdNaLQuK

Presenting a framework for an #AI-enabled bicycle-sharing system, to predict the count of bicycle rentals: โAI-driven #rentalbicyclesystem: An #Ensemblelearning approachโ by CK Long, TTT Hoang, GJ Rinco'n Aponte, V. Puri. ACSIS Vol. 38 p. 35โ39; https://t.co/IdoVb3P1rg

1/ Week 5 of Mira is live! Theme: Can AI give us the right answers? How accurate are they? Letโs dive into hallucinations and Miraโs solution.
#MiraNetwork #AI #LLM #EnsembleLearning #Hallucination #ConsensusAI
Mira network 5์ฃผ์ฐจ Voice of realm
5์ฃผ์ฐจ ๋ฏธ๋ผ์ ๋์ฅ๊ฒฝ ๊ฐ์ด ๋ ๋ ๋ณด์ค๊น์?
5์ฃผ์ฐจ ๋์ฅ๊ฒฝ์ ์ฃผ์ ๋ ๊ณผ์ฐ AI๋ ์ฐ๋ฆฌ์๊ฒ ์ฌ๋ฐ๋ฅธ ๋๋ต์ ํด์ค๊น? ์ผ๋งํผ ์ ํํ ๋๋ต์ ํด์ค๊น์
๋๋ค
์ ๊ฐ Mira network ์์๋ณด๊ธฐ 4ํ๊น์ง ์ฌ๋ฆฌ๋ฉด์ ์ง์์ ์ผ๋ก ์ด์ผ๊ธฐํ๋ ๋ถ๋ถ์ธ๋ฐ "ํ๊ฐ"์ด ์ด๋ ์ ๋ ์์ค๊น์ง ๋ํ๋๋์ง์ ๋ํ ์ด์ผ๊ธฐ์ฃ
Q: ๋ฏธ๊ตญ ์ฐ๋ฐฉ์ค๋น์ํ์ด ๋นํธ์ฝ์ธ์ ์ง๋ถ ์๋จ์ผ๋ก ์ฑํํ๋ค๋ฉฐ ๊ณง ๋นํธ์ฝ์ธ์ด $100000K?
๋ผ๋ ์ง๋ฌธ์ Mira์๊ฒ ํด ๋ณด์์ต๋๋ค
A: ๋ฏธ๊ตญ ์ฐ๋ฐฉ์ค๋น์ํ Fed๊ฐ ๋นํธ์ฝ์ธ์ ์ง๋ถ ์๋จ์ผ๋ก ์ฑํํ ์ค๋น๋ฅผ ๊ฑฐ์ ๋ง์ณค์ผ๋ฉฐ ๋นํธ์ฝ์ธ์ ์ง๋ถ ์๋จ์ผ๋ก ์ฑํํ๋ ๊ฒ์ ํผํ ์ ์๋ ๋ฌผ๊ฒฐ์ด๋ค. ๊ทธ๋์ ๋นํธ์ฝ์ธ์ด ๊ณง $1,000,000๋ก ์์น ํ ๊ฒ์ด๋ค
์์ฃผ ๊ฟ๋ง ๊ฐ์ ์ด์ผ๊ธฐ๊ณ ์ด๊ฒ "ํ๊ฐ"์ด ์๋์์ผ๋ฉด ์ข๊ฒ ๋ค๊ณ ์๊ฐํ์ง๋ง ์ฐ์ Fed๋ ๋นํธ์ฝ์ธ์ ์ง๋ถ ์๋จ์ผ๋ก ์ธ์ ํ ์๊ฐ์ด ์์ต๋๋ค. ๋ค๋ง Stable์ ๊ดํด์๋ ๊ด๋ํ ์
์ฅ์ ๋ณด์ด๊ณ ์์ฃ .
<์ ์ด๋ฐ ํ๊ฐ์ด ์ผ์ด๋๋ ๊ฒ์ผ๊น์?>
ํน์ GPT๋ Gemini๋ฅผ ์ฐ์๋ฉด์ ์ํ๋ ๋ต๋ณ์ด ๋์ฌ ๋๊น์ง ์ง๋ฌธ์ ํด๋ณด์ ์ ์์ผ์ ๊ฐ์? ๋๋ GPT๋ Gemini ๊ฐ์ AI๋ฅผ ํ์ฅ ์ํค๊ธฐ ์ํด ์ฌ๋ฌ ๊ฐ์ง ์๋๋ค์ ํด๋ณด์ ๋ถ๋ค์ด ์์ ๊ฒ์
๋๋ค์ ์ธ๊ณ ๋ฐฉ๋ํ ๋ฐ์ดํฐ ์
์ ๊ฐ์ง๊ณ ๊ทธ๊ฒ์ด ์ณ๊ณ ๊ทธ๋ฅธ์ง์ ๋ํด์ 100%๋ฅผ ๋ฌ์ฑํ๋ค๋ ๊ฒ์ด ์ด๋ ค์ด ์ด์ ์ฃ .
์ ๊ฐ์ ์ฌ๋ ๋๋ฌธ์
๋๋ค. GPT๋ฅผ ์ฐ์๋ค๋ณด๋ฉด ๋ถ๋ช
ํ๋ฆฐ ๋ต์ ๋ด๋์๋ค๊ณ ํ์์๋ ๋ถ๊ตฌํ๊ณ ๋ช ๋ฒ์ ๋ฌผ์ด๋ ๋์ผํ ๋๋ต์ ๋ด๋๊ฑฐ๋ ์์ ๊ฒ์ฆ์ ํ์ง ์๊ณ ๊ทธ๋ฅ ์ ํฌ๊ฐ ๋ฌผ์ด๋ณธ ์๋ฃ๋ค์ ์ฌ์ค๋ก ์ธ์ํ๋ "ํ๊ฐ" ํ์๋ค๋ ์กด์ฌํฉ๋๋ค
๋๋ ์ฌ๋๋ค์ด ์ ๋ฌผ์ด๋ณด์ง ์๋ ๋ด์ฉ๋ค์ ์์ง๊น์ง ํ์ต์ด ์ ๋์ด ์์ง ์์์ ๋ "ํ๊ฐ"์ด ๋ง์ด ์ผ์ด๋๋ ๋ชจ์ต๋ค์ ๋ณด์
๋๋ค.
๊ท์ฝ์ด๋ ๋ฒ๊ท๋ฅผ ํด์ํ๋ผ๊ณ ํ๋๋ ์ ๊ฐ์ ์ฌ๋ ์๋๋ฉด ๊ฑฐ์ ์ด๋ณผ ์ผ์ด ์๋ ๋ด์ฉ์ด๋ค๋ณด๋ ์ ํ๋๊ฐ ๋จ์ด์ง๋ ๊ฒ์ด์ฃ .
๊ทธ๋ ๋ค๋ฉด Mira๋ ์ด๊ฒ์ ์ด๋ป๊ฒ ํด๊ฒฐํ๋ ค๊ณ ํ๋ ๊ฒ์ผ๊น์?
<Mira์ ๋ค์ค ๋ชจ๋ธ ํฉ์ ์ ๊ทผ ๋ฐฉ์>
Mira๋ ์ฌ๋ฌ ๊ฐ์ ๋
๋ฆฝ์ AI ๋ชจ๋ธ๋ค์๊ฒ ๋์ผํ ์
๋ ฅ์ ๋ํด ๊ฐ๋ณ์ ์ผ๋ก ์ถ๋ก ๋ฐ ๋ต๋ณ์ ์ํํ ๋ค ๊ทธ ๊ฒฐ๊ณผ๋ฅผ "ํฉ์" (Consensus) ๋ฐฉ์์ผ๋ก ์ง๊ณํ์ฌ ์ต์ด์ ์ธ ๊ฒฐ๊ณผ๋ฌผ์ ๊ฒฐ์ ํ๋ ๋ฉ์ปค๋์ฆ ์
๋๋ค
์ด๋ฐ ๋ฐฉ์์ ์์๋ฅผ ๊ฐ์ฅ ์ ์ค๋ช
ํด์ฃผ์ ๋ถ์ ๊ธ์ด ์ข
๋ช
๋์ธ ๊ฒ ๊ฐ์ ์ข
๋ช
๋์ 5์ฃผ์ฐจ ๋์ฅ๊ฒฝ ์ฐธ์ฌ ๊ธ์ ์ฝ์ด๋ณด์๋ฉด ์ข์ ๊ฒ ๊ฐ์ต๋๋ค
๊ตฌ์ฒด์ ์ธ ์๋ ์๋ฆฌ๋ฅผ ์ดํด๋ณด๋ฉด
1) Claim ๋ณํ
์๋ณธ ํ
์คํธ๋ฅผ ๋
ผ๋ฆฌ์ ๋จ์์ "Claim"์ผ๋ก ๋ถํ , ์๋ฅผ ๋ค๋ฉด "X์ ์์ฅ ์ ์ ์จ์ 30%๋ค" ๊ฐ ํ๋์ ๋
ผ๋ฆฌ ๋จ์๊ฐ ๋๊ธฐ ๋๋ฌธ์ ์ด๋ฅผ ๋ถํดํ์ฌ ๊ฒ์ฆํ๋ Claim์ผ๋ก ๋ฐ๊พธ๋ ๊ฒ์ด์ฃ
2) ๋
ธ๋ ํ ๋น
๋ณํ๋ ๊ฐ Claim์ ๋คํธ์ํฌ์ ์กด์ฌํ๋ ์ฌ๋ฌ ๋
ธ๋ (AI ๋ชจ๋ธ)๋ก ์ค๋ฉ์ ์งํ
3) ๊ฐ๋ณ ๊ฒ์ฆ
๋๊ท๋ชจ LLM, ๋๋ฉ์ธ ํนํ ๋ชจ๋ธ, Retriveal -augmented ๋ชจ๋ธ ๋ฑ๋ฑ์ ํ์ฉํ์ฌ ์ฌ๋ฌ ๋ชจ๋ธ๋ก ๊ฒ์ฆ์ ์ค์
4) ํฉ์ ์ง๊ณ
๋ค์๊ฒฐ, ๊ฐ์คํฉ, ๊ธฐ๊ฐ ๊ฐ์ค์น ๋ฑ์ ์๊ณ ๋ฆฌ์ฆ์ ์ฌ์ฉํ์ฌ ์ต์ข
์ ์ผ๋ก 100%์ ์ ๋ ํฉ์๋ฅผ ๋์ถ (์ฌ๊ธฐ์ ํฉ์ ์๊ณ๊ฐ ์กฐ์ ๋์ด๋๋ฅผ ์ ํด์ผํจ)
5) ์ต์ข
์ถ๋ ฅ ๋ฐ ์ธ์ฆ์ (Certificate) ์ ์์์ด์ฃ
์ด๋ ๊ฒ ๋ง๋ค์ด์ง Mira network์ ์ด๋ก ์ ์ธ ๊ทผ๊ฐ์ ํ์ฌ pre - LLM ์์ ์ Hallucination์ ์์ ๊ธฐ ์ํด ์ ์๋์๋ ๋ฐฉ๋ฒ๋ค์ ์ฐจ์ฉํ ๊ฒ์
๋๋ค
์์๋ธ ํ์ต (Ensemble Learning), ๊ฒ์์ด๋ก , ํต๊ณ์ ๊ฒ์ฆ ๋ฑ๋ฑ์ ์ด๋ก ๋ค์ ๋ฐํ์ผ๋ก Mira network์ ๋ค์ค๋ชจ๋ธํฉ์๊ฐ ํ์ํ ๊ฒ์ด์ฃ
<Mira์ ๋ค์ค ๋ชจ๋ธ ํฉ์ ์ฅ๋จ์ ์?>
์ฅ์
1) ํ๊ฐ, ํธํฅ์ด ๋ํญ ๊ฐ์
2) ์ธ๋ถ ์ธํ๋ผ ์์กด๋ ์ถ์
3) ๋ค์ํ ๋๋ฉ์ธ ๋ชจ๋ธ ๊ฐ ์๋์ง
4) ๊ฒ์ฆ ๊ณผ์ ํฌ๋ช
์ฑ ํ๋ณด
๋จ์
1) ์ค๋ฉ ์ค๋ฒํค๋: ๊ณผ๋ํ Latency ์ ๋ฐ ๊ฐ๋ฅ
2) ํฉ์ ์๊ณ๊ฐ ์กฐ์ ๋์ด๋์ ๋ฐ๋ผ ๋คํธ์ํฌ์ ์์ ์ฑ ๋ฐ Latency ์ฐ๋ ค
3) ๋น์ฉ, ์คํ
์ดํน ๋ถ๋ด
๋ฑ๋ฑ์ด ์์ต๋๋ค
์๋ง ๋ค์ ํธ์ ๋ค๋ฃจ๊ฒ ๋ ๋ด์ฉ์ ๋จ์ ์ค์์ ๋น์ฉ, ์คํ
์ดํน ๋ถ๋ด์ด ๋ ๊ฒ ๊ฐ์๋ฐ
์ด ๋ถ๋ถ์ Mira network๊ฐ ์ฑ๊ณตํ๊ธฐ ์ํด์ ํ ํฐ์ด ์ด๋ค ์์ผ๋ก ์ธ๋ชจ ์๋ flywheel์ ๊ฐ์ง์ง, usecase๋ฅผ ๊ฐ์ง ์ง๋ฅผ ๏ฟฝ๏ฟฝ๋ฎค๋ํฐ๋ ํจ๊ป ๊ณ ๋ฏผํด๋ณด์์ผ ํ๋ค๋ ๊ฒ์ ์๋ฏธํ๋ค๊ณ ๋ด
๋๋ค
@miranetwork @MiraNetworkCN @Edward__Park @jeg6322 @hyeon__dev @karansirdesai @stone_gettings
#voiceofrealm #mira

Read #HighlyAccessedArticle โOptimized Ensemble Learning Approach with Explainable AI for Improved Heart Disease Predictionโ by Ibomoiye Domor Mienye and Nobert Jere.
See more details at:
https://t.co/39XFIjNsVs
#heartdisease
#ensemblelearning
@ComSciMath_Mdpi

Today
> Completed the AdaBoost algorithm
> Adjusting weights, boosting weak learners, and focusing on misclassified samples to improve accuracy.
#AdaBoost #MachineLearning #EnsembleLearning

๐#MDPIfutureinternet [New Editor's Choice Articles in the Second Half of 2023]
๐Title: Enhancing Collaborative Filtering-Based #RecommenderSystem Using #SentimentAnalysis
๐ https://t.co/aOw5gftN5x
#deeplearning #collaborativefiltering #ensemblelearning
@ComSciMath_Mdpi
![FutureInternet6's tweet photo. ๐#MDPIfutureinternet [New Editor's Choice Articles in the Second Half of 2023]
๐Title: Enhancing Collaborative Filtering-Based #RecommenderSystem Using #SentimentAnalysis
๐ https://t.co/aOw5gftN5x
#deeplearning #collaborativefiltering #ensemblelearning
@ComSciMath_Mdpi https://t.co/o36cE0fS2Q](https://pbs.twimg.com/media/GqQUMI0bAAAgTS7.png)
We gladly present you our new J.UCS issue today! Find five exciting articles on current topics like #JavaGenerics #ModelDrivenEngineering #EnsembleLearning #RecommenderSystems and #ConvolutionalNeuralNetwork here:
https://t.co/gw2JLT38Jx
We thank the authors and reviewers!

Day 81/100 ๐
โ
Learned and implemented Ensemble Learning ๐ฏ
โ
Did a chest and triceps workout ๐ช
#100DaysOfCode #ML #EnsembleLearning #AI #Coding #Fitness

๐Today, I explored Ensemble Learning
๐น Voting Ensemble โ Combines multiple models
๐น Bagging โ Reduces variance with random subsets
๐น Bagging Classifier โ For classification tasks
๐น Bagging Regressor โ For regression problems
#MachineLearning #EnsembleLearning #DataScience

ูู ุฑููู ุฃููุงุน ุงููู
ุงุฐุฌ ุงูุงุญุตุงุฆูุฉ ุดุงุฆุนุฉ ุงูุงุณุชุฎุฏุงู
ูู ุนููู
ุงูุจูุงูุงุช
#StatisticalModels #DataScience #MachineLearning #AI #LinearRegression #LogisticRegression #TimeSeries #SurvivalAnalysis #DecisionTree #NeuralNetworks #EnsembleLearning #MultivariateAnalysis

Top Books on #EnsembleLearning! #BigData #Analytics #DataScience #AI #MachineLearning #IoT #IIoT #Python #RStats #TensorFlow #Java #JavaScript #ReactJS #GoLang #CloudComputing #Serverless #DataScientist #Linux #Books #Programming #Coding #100DaysofCode
https://t.co/BdYdNaLQuK

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