Described the use of Root Mean Square Logarithmic Error (RMSLE) as cost function in machine learning (ML).
1. What is it?
2. How is it different from RMSE?
3. When to use it?
Described the use of Root Mean Square Logarithmic Error (RMSLE) as cost function in machine learning (ML).
1. What is it?
2. How is it different from RMSE?
3. When to use it?
Extract Big Returns from Investments in Big Data and Predictive Analytics in ...SAP Analytics
Most companies in the oil and gas, utilities and chemical process industries benefit significantly from global markets. They are using real-time data and analytics to solve key challenges in hotly competitive global markets.
Gentlest Introduction to Tensorflow - Part 3Khor SoonHin
Articles:
* https://medium.com/all-of-us-are-belong-to-machines/gentlest-intro-to-tensorflow-part-3-matrices-multi-feature-linear-regression-30a81ebaaa6c
* https://medium.com/all-of-us-are-belong-to-machines/gentlest-intro-to-tensorflow-4-logistic-regression-2afd0cabc54
Video: https://youtu.be/F8g_6TXKlxw
Code: https://github.com/nethsix/gentle_tensorflow
In this part, we:
* Use Tensorflow for linear regression models with multiple features
* Use Tensorflow for logistic regression models with multiple features. Specifically:
* Predict multi-class/discrete outcome
* Explain why we use cross-entropy as cost function
* Explain why we use softmax
* Tensorflow Cheatsheet #1
* Single feature linear regression
* Multi-feature linear regression
* Multi-feature logistic regression
How to Maximize Revenues on Your Customer Loyalty Program using Predictive An...Tatvic Analytics
Are you sending out discount coupons to all your customers to encourage them to carry out future purchases? If yes, then your customer loyalty program is leaking revenue.
From an economic perspective, sending out discount coupons isn't the most preferred approach that the marketer must take. In this webinar, we show how to use Predictive Analytics to segment customers based on your Web Analytics and CRM Data. We can then use this unprecedented insight to target discount coupons to the most appropriate segment and thus optimize revenues.
We will cover
* How to import your Google Analytics data into R
* How to build a Predictive Model using this data
* How to implement this model in a live environment
Watch full webinar video - http://www.tatvic.com/webinar/maximize-revenue-on-customer-loyalty-program-with-predictive-analytics/
Extract Big Returns from Investments in Big Data and Predictive Analytics in ...SAP Analytics
Most companies in the oil and gas, utilities and chemical process industries benefit significantly from global markets. They are using real-time data and analytics to solve key challenges in hotly competitive global markets.
Gentlest Introduction to Tensorflow - Part 3Khor SoonHin
Articles:
* https://medium.com/all-of-us-are-belong-to-machines/gentlest-intro-to-tensorflow-part-3-matrices-multi-feature-linear-regression-30a81ebaaa6c
* https://medium.com/all-of-us-are-belong-to-machines/gentlest-intro-to-tensorflow-4-logistic-regression-2afd0cabc54
Video: https://youtu.be/F8g_6TXKlxw
Code: https://github.com/nethsix/gentle_tensorflow
In this part, we:
* Use Tensorflow for linear regression models with multiple features
* Use Tensorflow for logistic regression models with multiple features. Specifically:
* Predict multi-class/discrete outcome
* Explain why we use cross-entropy as cost function
* Explain why we use softmax
* Tensorflow Cheatsheet #1
* Single feature linear regression
* Multi-feature linear regression
* Multi-feature logistic regression
How to Maximize Revenues on Your Customer Loyalty Program using Predictive An...Tatvic Analytics
Are you sending out discount coupons to all your customers to encourage them to carry out future purchases? If yes, then your customer loyalty program is leaking revenue.
From an economic perspective, sending out discount coupons isn't the most preferred approach that the marketer must take. In this webinar, we show how to use Predictive Analytics to segment customers based on your Web Analytics and CRM Data. We can then use this unprecedented insight to target discount coupons to the most appropriate segment and thus optimize revenues.
We will cover
* How to import your Google Analytics data into R
* How to build a Predictive Model using this data
* How to implement this model in a live environment
Watch full webinar video - http://www.tatvic.com/webinar/maximize-revenue-on-customer-loyalty-program-with-predictive-analytics/
[Ubicomp'15]SakuraSensor: Quasi-Realtime Cherry-Lined Roads Detection throug...Ubi NAIST
SakuraSensor, a system which senses and shares the information of roads with flowering cherries by leveraging car-mounted smart-phones.
Honorable Mention Award of UbiComp2015.
セル生産方式におけるロボットの活用には様々な問題があるが,その一つとして 3 体以上の物体の組み立てが挙げられる.一般に,複数物体を同時に組み立てる際は,対象の部品をそれぞれロボットアームまたは治具でそれぞれ独立に保持することで組み立てを遂行すると考えられる.ただし,この方法ではロボットアームや治具を部品数と同じ数だけ必要とし,部品数が多いほどコスト面や設置スペースの関係で無駄が多くなる.この課題に対して音𣷓らは組み立て対象物に働く接触力等の解析により,治具等で固定されていない対象物が組み立て作業中に運動しにくい状態となる条件を求めた.すなわち,環境中の非把持対象物のロバスト性を考慮して,組み立て作業条件を検討している.本研究ではこの方策に基づいて,複数物体の組み立て作業を単腕マニピュレータで実行することを目的とする.このとき,対象物のロバスト性を考慮することで,仮組状態の複数物体を同時に扱う手法を提案する.作業対象としてパイプジョイントの組み立てを挙げ,簡易な道具を用いることで単腕マニピュレータで複数物体を同時に把持できることを示す.さらに,作業成功率の向上のために RGB-D カメラを用いた物体の位置検出に基づくロボット制御及び動作計画を実装する.
This paper discusses assembly operations using a single manipulator and a parallel gripper to simultaneously
grasp multiple objects and hold the group of temporarily assembled objects. Multiple robots and jigs generally operate
assembly tasks by constraining the target objects mechanically or geometrically to prevent them from moving. It is
necessary to analyze the physical interaction between the objects for such constraints to achieve the tasks with a single
gripper. In this paper, we focus on assembling pipe joints as an example and discuss constraining the motion of the
objects. Our demonstration shows that a simple tool can facilitate holding multiple objects with a single gripper.
【DLゼミ】XFeat: Accelerated Features for Lightweight Image Matchingharmonylab
公開URL:https://arxiv.org/pdf/2404.19174
出典:Guilherme Potje, Felipe Cadar, Andre Araujo, Renato Martins, Erickson R. ascimento: XFeat: Accelerated Features for Lightweight Image Matching, Proceedings of the 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2023)
概要:リソース効率に優れた特徴点マッチングのための軽量なアーキテクチャ「XFeat(Accelerated Features)」を提案します。手法は、局所的な特徴点の検出、抽出、マッチングのための畳み込みニューラルネットワークの基本的な設計を再検討します。特に、リソースが限られたデバイス向けに迅速かつ堅牢なアルゴリズムが必要とされるため、解像度を可能な限り高く保ちながら、ネットワークのチャネル数を制限します。さらに、スパース下でのマッチングを選択できる設計となっており、ナビゲーションやARなどのアプリケーションに適しています。XFeatは、高速かつ同等以上の精度を実現し、一般的なラップトップのCPU上でリアルタイムで動作します。
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こうした不動産価格推定の研究は,われわれが初めたわけではい
しかもこれらは全て一般住宅向けである
ただし,関係ないわけではなく,どんな特徴量を使うかという点では参考になります
たとえば風水,環境変数
Victor Gan, Vaishali Agarwal, Ben Kim: DATA MINING ANALYSIS AND PREDICTIONS OF REAL ESTATE PRICES, Issues in Information System,Volume 16, Issue IV, pp.30-36 (2015)
Chih-Hung Wu, Chi-Hua Li, I-Ching Fang, Chin-Chia Hsu, Wei-Ting Lin, Chia-Hsiang Wu: HYBRID GENETIC-BASED SUPPORT VECTOR REGRESSION WITH FENG SHUI THEORY FOR APPRAISING REAL ESTATE PRICE, 2009 First Asian Conference on Intelligent Information and Database Systems, pp.295-300 (2009)
Vincenza Chiarazzo, Leonardo Caggiani, Mario Marinelli, Michele Ottomanelli: A Neural Network based model for real estate price estimation considering environmental quality of property location, Transportation Research Procedia 3, pp.810-817 (2014)
株式会社リブセンス News Release 2015年11月5日
未来型不動産サービス「IESHIL(イエシル)」掲載物件の推定賃料がわかる新機能を追加
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★プライバシーの配慮
★屋内のものが主流
カメラなし屋外がチャレンジング
Zeng, Chengbin ; Ling, Charles X.:A Reliable People Counting System via Multiple Cameras,ACM Transactions on Intelligent Systems and Technology (TIST) (Volume:3 , Issue: 2 )
Wahl, F.; Milenkovic, M.; Amft, O:A Distributed PIR-based Approach for Estimating People Count in Office Environments,Computational Science and Engineering (CSE), 2012 IEEE 15th International Conference on