Risk Classification in Life Insurance:Huebner International Series on Risk, Insurance and Economic Security. Softcover reprint of the original 1st ed. 1983
Robust Regression Methods for Insurance Risk Classification:Robust Methods Using Multinomial Logistic Risk Insurance Esteban Flores
Risk Classification in Life Insurance:
Copula Based Risks Classification Models for General Insurance:A Kenyan General Insurance Business Case Joseph Kyalo Mung´atu
Already provoking debate and garnering significant attention in France and within the wine world, Vino Business is a surprising and eye-opening book about the dark side of French wine by acclaimed investigative journalist Isabelle Saporta. While Bordeaux has been a bastion of winemaking tradition and excellence for centuries, in recent decades the industry has changed dramatically under the influence of large-scale international investors. French insurance companies, international fashion houses, and Chinese businessmen are all speculating on the area´s wines and land, some of whose value has increased tenfold in the last decade alone. Saporta investigates in detail the 2012 classification of the wines of Saint-Émilion, the most prestigious appellation of Bordeaux´s right bank, which has come into disrepute, not least because the scoring system was changed in order to give points for a châteaux´s lecture facilities and the size of its parking lot. A shocking exposé of the French wine world and a cri de coeur for the lost values of traditional winemaking, Vino Business pulls back the curtain on the secret domain of Bordeaux, a land ever more in thrall to the grapes of wealth. 1. Language: English. Narrator: Kristin Kalbli. Audio sample: http://samples.audible.de/bk/adbl/025368/bk_adbl_025368_sample.mp3. Digital audiobook in aax.
Algorithms of the Intelligent Web, Second Edition teaches you how to create machine learning applications that crunch and wrangle data collected from users, web applications, and website logs. In this totally revised edition, you´ll look at intelligent algorithms that extract real value from data. Key machine learning concepts are explained with code examples in Python´s scikit-learn. This book guides you through algorithms to capture, store, and structure data streams coming from the web. You´ll explore recommendation engines and dive into classification via statistical algorithms, neural networks, and deep learning. Valuable insights are buried in the tracks web users leave as they navigate pages and applications. You can uncover them by using intelligent algorithms like the ones that have earned Facebook, Google, and Twitter a place among the giants of web data pattern extraction. This audiobook includes:An introduction to machine learningExtracting structure from dataDeep learning and neural networksHow recommendation engines work Knowledge of Python is assumed for the listener. Douglas McIlwraith is a machine learning expert and data science practitioner in the field of Online advertising. Dr. Haralambos Marmanis is a pioneer in the adoption of machine learning techniques for industrial solutions. Dmitry Babenko designs applications for banking, insurance, and supply-chain management. Table of Contents:1. Building applications for the intelligent web 2. Extracting structure from data: clustering and transforming your data 3. Recommending relevant content 4. Classification: placing things where they belong 5. Case study: click prediction for Online advertising 6. Deep learning and neural networks 7. Making the right choice 8. The future of the intell 1. Language: English. Narrator: Mark Thomas. Audio sample: http://samples.audible.de/bk/acx0/130626/bk_acx0_130626_sample.mp3. Digital audiobook in aax.