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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.

http://www.ickn.org/documents/COINs2010_Twitter4.pdf

Predicting Stock Market Indicators Through Twitter “I hope it is not as bad as I fear”

Introduction: At the Tipping Point 1. COINs and Their Benefits 2. Collaborative Innovation Through Swarm Creativity 3. The DNA of COINS 4. Ethical Codes in Small Worlds 5. Real-Life Examples: Lessons Learned from COINs 6. COINs and Communications Technology Appendix A CKN Appendix B TeCFlow Appendix C KFO

/pdf/swarm-creativity-competitive-advantage-through-collaborative-2684yctwpo.pdf

Swarm Creativity: Competitive Advantage through Collaborative Innovation Networks

In the shades of the uncanny valley: An experimental study of human–chatbot interaction

– Social media provide an impressive amount of data about users and their interactions, thereby offering computer and social scientists, economists, and statisticians – among others – new opportunities for research. Arguably, one of the most interesting lines of work is that of predicting future events and developments from social media data. However, current work is fragmented and lacks of widely accepted evaluation approaches. Moreover, since the first techniques emerged rather recently, little is known about their overall potential, limitations and general applicability to different domains. Therefore, better understanding the predictive power and limitations of social media is of utmost importance. , – Different types of forecasting models and their adaptation to the special circumstances of social media are analyzed and the most representative research conducted up to date is surveyed. Presentations of current research on techniques, methods, and empirical studies aimed at the prediction of future or current events from social media data are provided. , – A taxonomy of prediction models is introduced, along with their relative advantages and the particular scenarios where they have been applied to. The main areas of prediction that have attracted research so far are described, and the main contributions made by the papers in this special issue are summarized. Finally, it is argued that statistical models seem to be the most fruitful approach to apply to make predictions from social media data. , – This special issue raises important questions to be addressed in the field of social media-based prediction and forecasting, fills some gaps in current research, and outlines future lines of work.

/pdf/the-power-of-prediction-with-social-media-30bfxrs0pm.pdf

The power of prediction with social media

Collaborative Innovation Networks (COINs) are groups of self-motivated individuals from various parts of an organization or from multiple organizations, empowered by the Internet, who work together on a new idea, driven by a common vision. In this paper we report first results of a project that examines innovation networks by analyzing the e-mail archives of some W3C (WWW consortium) working groups. These groups exhibit ideal characteristics for our purpose, as they form truly global networks working together over the Internet to develop next generation technologies. We first describe the software tools we developed to visualize the temporal communication flow, which represent communication patterns as directed acyclic graphs, We then show initial results, which revealed significant variations between the communication patterns and network structures of the different groups., We were also able to identify distinctive communication patterns among group leaders, both those who were officially appointed and other who were assuming unofficial coordinating roles.

/pdf/visualization-of-communication-patterns-in-collaborative-3b7jxuw2d6.pdf

Peter A. Gloor

Papers

Predicting Stock Market Indicators Through Twitter “I hope it is not as bad as I fear”

Swarm Creativity: Competitive Advantage through Collaborative Innovation Networks

In the shades of the uncanny valley: An experimental study of human–chatbot interaction

The power of prediction with social media

Visualization of Communication Patterns in Collaborative Innovation Networks - Analysis of Some W3C Working Groups