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Using Multivariate Statistics

Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

I have developed "tennis elbow" from lugging this book around the past four weeks, but it is worth the pain, the effort, and the aspirin. It is also worth the (relatively speaking) bargain price. Including appendixes, this book contains 894 pages of text. The entire panorama of the neural sciences is surveyed and examined, and it is comprehensive in its scope, from genomes to social behaviors. The editors explicitly state that the book is designed as "an introductory text for students of biology, behavior, and medicine," but it is hard to imagine any audience, interested in any fragment of neuroscience at any level of sophistication, that would not enjoy this book. The editors have done a masterful job of weaving together the biologic, the behavioral, and the clinical sciences into a single tapestry in which everyone from the molecular biologist to the practicing psychiatrist can find and appreciate his or

Principles of Neural Science

How has Japan become a major economic power, a world leader in the automotive and electronics industries? What is the secret of their success? The consensus has been that, though the Japanese are not particularly innovative, they are exceptionally skilful at imitation, at improving products that already exist. But now two leading Japanese business experts, Ikujiro Nonaka and Hiro Takeuchi, turn this conventional wisdom on its head: Japanese firms are successful, they contend, precisely because they are innovative, because they create new knowledge and use it to produce successful products and technologies. Examining case studies drawn from such firms as Honda, Canon, Matsushita, NEC, 3M, GE, and the U.S. Marines, this book reveals how Japanese companies translate tacit to explicit knowledge and use it to produce new processes, products, and services.

The knowledge-creating company : how Japanese companies create the dynamics of innovation

http://cfile28.uf.tistory.com/attach/23159E3D57117DF7107077

Twitter mood predicts the stock market.

iQuest is a novel software system to improve understanding of organizational phenomena with greater precision, clarity, and granularity than has previously been possible. It permits to gain new insights into organizational behavior, addressing issues such as tracking information while respecting privacy, comparing different interaction channels, network membership, and correlating organizational performance and creativity. It extends automatic visualization of social networks by mining communication archives such as e-mail and blogs through including analysis of the contents of those archives.

/pdf/analyzing-actors-and-their-discussion-topics-by-semantic-5ds3k1o945.pdf

Analyzing Actors and Their Discussion Topics by Semantic Social Network Analysis

We show that it is possible to identify individual personality traits and measure group performance in a Post-anesthesia Care Unit (PACU) using wearable sensors.We instrumented a group of 67 nurses working in the PACU of a Boston area hospital with sociometric badges capable of measuring physical activity, speech activity, face-to-face interaction, and physical proximity. Using the data collected with these sensors we were able to estimate the daily average length of stay (LOS) and number of delays.

/pdf/wearable-sensors-for-pervasive-healthcare-management-21n49wfr9d.pdf

Wearable sensors for pervasive healthcare management

This paper describes early work trying to predict financial market movement such as gold price, crude oil price, currency exchange rates and stock market indicators by analyzing Twitter posts. We collected Twitter feeds for 5 months obtaining a large set of emotional retweets originating from within the US, from which six public opinion time series containing the keywords “dollar% t ”, “$% t ”, “gold% t ”, “oil% t , “job% t ” and “economy% t ” were extracted. Our results show that these variables are correlated to and even predictive of the financial market movement. Except “$% t ”, all other five public opinion time series are identified by a Granger-causal relationship with certain market movements. It is demonstrated that daily changes in the volume of economic topic retweeting seem to match the value shift occurring in the corresponding market next day.

/pdf/predicting-asset-value-through-twitter-buzz-2cj1afmrz1.pdf

Predicting Asset Value through Twitter Buzz

https://www.wu.ac.at/fileadmin/wu/d/i/entrep/4_forschung/full-text-papers/kratzer_lettl_franke_gloor_2015_network_position_lead_user.pdf

Peter A. Gloor

Papers

Analyzing Actors and Their Discussion Topics by Semantic Social Network Analysis

Wearable sensors for pervasive healthcare management

Predicting Asset Value through Twitter Buzz

The Social Network Position of Lead Users

Hypertext und Hypermedia