We present a machine-checked, formal proof of PAC learnability of the concept class of decision stumps. A formal proof has every step checked and justified using fundamental axioms of mathematics. We construct and check our proof using the Lean theorem prover. Though such a proof appears simple, a few analytic and measure-theoretic subtleties arise when carrying it out fully formally. We explain how we can cleanly separate out the parts that deal with these subtleties by using Lean features and a category theoretic construction called the Giry monad.

Based on the online transaction data of COSCO group's centralized procurement platform, this paper studies the clustering method of time series type data. The different methods of similarity calculation, different clustering methods with different K values are analysed, and the best clustering method suitable for centralized purchasing data is determined. The company list under the corresponding cluster is obtained. The time series motif discovery algorithm is used to model the centroid of each cluster. Through ARIMA method, we also made 12 periods of prediction for the centroid of each category. This paper constructs a matrix of "Customer Lifecycle Theory - Five Elements of Marketing ", and puts forward corresponding marketing suggestions for customers at different life cycle stages.

About US: Wise Guy Reports is part of the Wise Guy Consultants Pvt. Ltd. and offers premium progressive statistical surveying, market research reports, analysis & forecast data for industries and governments around the globe.

About US: Wise Guy Reports is part of the Wise Guy Consultants Pvt. Ltd. and offers premium progressive statistical surveying, market research reports, analysis & forecast data for industries and governments around the globe.

San Jose, CA, Oct. 30, 2019 (GLOBE NEWSWIRE) -- via NEWMEDIAWIRE --Sonasoft Corp. (OTCQB: SSFT), a leader in innovative artificial intelligence (AI), today announced that it has won a contract with Padmini VNA, a manufacturer of products for original equipment manufacturers, for the Company's artificial intelligence (AI) solution, NuGene. The OEM manufacturer will deploy NuGene across an army of robots by O&M Robotics, a partner of Padmini VNA, enabling these robots to autonomously navigate and clean solar panels, allowing for millions of dollars in maintenance to be saved. Solar panels' efficiency can diminish by as much as 20% in domestic installations and as high as 60% in commercial installations. The number is too large to be ignored especially for commercial installations. This situation has encouraged the need for an efficient and cost-effective system to clean the surface of solar panels.

Artificial intelligence, machine learning, and deep learning solutions are some of the hottest buzzwords in today's corporate landscape. These technologies are reshaping the corporate landscape with their capability to provide innovative solutions to some long-standing problems. In today's quickly-evolving corporate landscape, companies must often engage in intense competition to secure users and customers. In the age of big data and in-depth analysis of customer behavior, artificial intelligence (AI) and machine learning (ML) solutions are emerging as the de facto way for companies to gain a competitive edge. Today, it is easier to harvest large amounts of data from the customer. The advancement of the AI field has resulted in the creation and adoption of machine learning. Machine learning was then discovered to be a good fit for the corporate landscape, providing cost-effective solutions to problems that previously required a lot of resources.

Global Artificial Intelligence in BFSI Market size in 2017 is 2.50 million US$ and it is expected to reach 19.80 million US$ by the end of 2026 with a CAGR of 29.52% during 2017 -2026. Artificial intelligence (AI) in BFSI refers to the simulation of human intelligence into machines with the help of sophisticated machine learning, deep learning, chat-bots, cognitive computing, and natural language processing algorithms that help in customer relationship management, communication, and recruitment & wealth management. Artificial intelligence in BFSI is driven mainly by digital data .Artificial Intelligence (AI) is fast evolving as the go-to technology for banks across the world to personalize experience for individuals. Positive rise of AI-based application in BFSI such as customer support, fraud detection, improving employee efficiency, reduce fraud and security risks. Growing adoption of smart devices and growing penetration of internet services across the globe is fuelling amount of the data. A number of financial services institution are already generating value from artificial intelligence.

Strong writing can determine whether a Hollywood show turns out like Game of Thrones or Pee-Wee Herman's Big Adventure. That's why Ron Howard and Brian Grazer's Imagine Entertainment has opened an incubator for writers called Imagine Impact. The film and TV industries employ 2.6 million people in the U.S. alone, and those businesses generate $177 billion a year in wages. But surfacing new writers can be a haphazard process. Imagine Impact, the division of a film production company with dozens of Oscar-nominated films, wants to create a pipeline of strong writers.

User identity linkage is a task of recognizing the identities of the same user across different social networks (SN). Previous works tackle this problem via estimating the pairwise similarity between identities from different SN, predicting the label of identity pairs or selecting the most relevant identity pair based on the similarity scores. However, most of these methods ignore the results of previously matched identities, which could contribute to the linkage in following matching steps. To address this problem, we convert user identity linkage into a sequence decision problem and propose a reinforcement learning model to optimize the linkage strategy from the global perspective. Our method makes full use of both the social network structure and the history matched identities, and explores the long-term influence of current matching on subsequent decisions. We conduct experiments on different types of datasets, the results show that our method achieves better performance than other state-of-the-art methods.

Continuing Our series of posts on how to interpret Machine Learning algorithms and predictions. Part 0 (optional) -- What is Data Science and the Data Scientist Part 1 -- Introduction to Interpretability Part 1.5 (optional) -- A Brief History of Statistics (May be useful to understand this post) Part 2 -- (this post) Interpreting models of high bias and low variance. Part 4 -- Is it possible to resolve the trade-off between bias and variance? Using Shapley to finally open the black box! In this post we will focus on the interpretation of high bias and low variance models, as we explained in the previous post, these algorithms are the easiest to interpret so assume several prerequisites in the data.