2646 search results for "gis"

Machine Learning Ex 5.1 – Regularized Linear Regression

October 25, 2011
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Machine Learning Ex 5.1 – Regularized Linear Regression

The first part of the Exercise 5.1 requires to implement a regularized version of linear regression. Adding regularization parameter can prevent the problem of over-fitting when fitting a high-order polynomial. Read More: 194 Words Totally

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Vanilla C code for the Stochastic Simulation Algorithm

October 24, 2011
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Vanilla C code for the Stochastic Simulation Algorithm

The Gillespie stochastic simulation algorithm (SSA) is the gold standard for simulating state-based stochastic models. If you are a R buff, a SSA novice and want to get quickly up and running stochastic models (in particular ecological models) that are not … Continue reading →

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understanding computational Bayesian statistics: a reply from Bill Bolstad

October 23, 2011
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understanding computational Bayesian statistics: a reply from Bill Bolstad

Bill Bolstad wrote a reply to my review of his book Understanding computational Bayesian statistics last week and here it is, unedited except for the first paragraph where he thanks me for the opportunity to respond, “so readers will see that the book has some good features beyond having a “nice cover”.” (!) I simply processed

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Using Sweave with XeLaTeX

October 23, 2011
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Using Sweave with XeLaTeX

Using R with LaTeX via Sweave is a great way to create reproducible output. However, using specific fonts, e.g. your corporate fonts, can be painful with pdflatex. Over the last few weeks I have fallen in love with the TeX formatXeLaTeX and its XeTeX e...

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Backtesting Part 4: random strategies

October 21, 2011
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Backtesting Part 4: random strategies

Note: This post is NOT financial advice!  This is just a fun way to explore some of the capabilities R has for importing and manipulating data.   In part 2, we found that our 200-day high, hold 100 days strategy yielded average annual return...

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Support Vector Machines in R (a course by Lutz Hamel)

October 19, 2011
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Support vector machines (SVM’s) are the “big iron” of the data mining world, especially suited for extreme data intensive tasks like image classification, biosequence processing, handwriting recognition, etc. Dr. Lutz Hamel, author of “Knowledge Discovery with Support Vector Machines”, presents his online course “Introduction to Support Vector Machines In R” November 18 – December 16. “Support Vector Machines in...

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Revolution Newsletter: October 2011

October 17, 2011
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The most recent edition of the Revolution Newsletter is out. The news section is below, and you can read the full October edition (with highlights from this blog and community events) online. You can subscribe to the Revolution Newsletter to get it monthly via email. Applications of R Contest: Deadline October 31. Revolution Analytics is offering $20,000 in prizes...

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Backtesting a Simple Stock Trading Strategy: Part 3

October 17, 2011
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Backtesting a Simple Stock Trading Strategy: Part 3

Note: This post is NOT financial advice!  This is just a fun way to explore some of the capabilities R has for importing and manipulating data.   In a previous post, I examined a simple stock trading strategy: Find the high point over the la...

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Free auditing of Stanford AI and Machine Learning Courses w/Peter Norvig

October 14, 2011
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Free auditing of Stanford AI and Machine Learning Courses w/Peter Norvig

Just wanted to notify viewers of a few great courses that are being offered free for auditing and/or participation by well known industry experts, including co-author of the classic text on AI, 'Artificial Intelligence: A Modern Approach,' Peter Norvig...

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Trading Mean Reversion with Augen Spikes

October 14, 2011
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Trading Mean Reversion with Augen Spikes

One of the more interesting things I have come across is the idea of looking at price changes in terms of recent standard deviation, a concept put forward by Jeff Augen. The gist is to express a close to close return as a function of the standard devia...

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