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ai @ wvu

Modeling Intelligence Lab ("the MILL"). Thursday, November 20, 2014. Table of Rank Sums Across All Datasets (Random Forest). 66 : Default Cur - Cur. 41 : Tuned Prev - Cur. 28 : Tuned Cur - Cur. 0 : Default Prev - Cur. Table of Rank Sums Across All Datasets (Logistic Regression). 66 : Default Cur - Cur. 41 : Tuned Prev - Cur. 28 : Tuned Cur - Cur. 0 : Default Prev - Cur. Table of Rank Sums Across All Datasets (Bernoulli NB). 41 : Tuned Cur - Cur. 28 : Tuned Prev - Cur. 20 : Default Cur - Cur. 23 # CT1* xo...

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ai @ wvu | ai-at-wvu.blogspot.com Reviews
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Modeling Intelligence Lab (the MILL). Thursday, November 20, 2014. Table of Rank Sums Across All Datasets (Random Forest). 66 : Default Cur - Cur. 41 : Tuned Prev - Cur. 28 : Tuned Cur - Cur. 0 : Default Prev - Cur. Table of Rank Sums Across All Datasets (Logistic Regression). 66 : Default Cur - Cur. 41 : Tuned Prev - Cur. 28 : Tuned Cur - Cur. 0 : Default Prev - Cur. Table of Rank Sums Across All Datasets (Bernoulli NB). 41 : Tuned Cur - Cur. 28 : Tuned Prev - Cur. 20 : Default Cur - Cur. 23 # CT1* xo...
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9 new results format
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ai @ wvu | ai-at-wvu.blogspot.com Reviews

https://ai-at-wvu.blogspot.com

Modeling Intelligence Lab ("the MILL"). Thursday, November 20, 2014. Table of Rank Sums Across All Datasets (Random Forest). 66 : Default Cur - Cur. 41 : Tuned Prev - Cur. 28 : Tuned Cur - Cur. 0 : Default Prev - Cur. Table of Rank Sums Across All Datasets (Logistic Regression). 66 : Default Cur - Cur. 41 : Tuned Prev - Cur. 28 : Tuned Cur - Cur. 0 : Default Prev - Cur. Table of Rank Sums Across All Datasets (Bernoulli NB). 41 : Tuned Cur - Cur. 28 : Tuned Prev - Cur. 20 : Default Cur - Cur. 23 # CT1* xo...

INTERNAL PAGES

ai-at-wvu.blogspot.com ai-at-wvu.blogspot.com
1

ai @ wvu: November 2014

http://ai-at-wvu.blogspot.com/2014_11_01_archive.html

Modeling Intelligence Lab ("the MILL"). Thursday, November 20, 2014. Table of Rank Sums Across All Datasets (Random Forest). 66 : Default Cur - Cur. 41 : Tuned Prev - Cur. 28 : Tuned Cur - Cur. 0 : Default Prev - Cur. Table of Rank Sums Across All Datasets (Logistic Regression). 66 : Default Cur - Cur. 41 : Tuned Prev - Cur. 28 : Tuned Cur - Cur. 0 : Default Prev - Cur. Table of Rank Sums Across All Datasets (Bernoulli NB). 41 : Tuned Cur - Cur. 28 : Tuned Prev - Cur. 20 : Default Cur - Cur.

2

ai @ wvu: XOMO and POM PAPER

http://ai-at-wvu.blogspot.com/2014/10/xomo-and-pom-paper.html

Modeling Intelligence Lab ("the MILL"). Friday, October 3, 2014. XOMO and POM PAPER. Base Line xomofl500 m 13 12 10 6 #. CT0* xomofl500 m 12 11 9 7 #. CT0 xomofl500 m 0 1 0 0 #. CT1* xomofl500 m 3 7 1 4 #. CT1 xomofl500 m 11 7 4 3 #. NSGA xomofl500 m 0 48 0 0 #. Base Line xomofl500 q 11 0 10 14 #. CT0* xomofl500 q 12 0 11 13 #. CT0 xomofl500 q 0 0 0 1 #. CT1* xomofl500 q 3 0 1 8 #. CT1 xomofl500 q 7 0 4 9 #. NSGA xomofl500 q 1 21 0 13 #. Base Line xomofl500 w 49 12 47 41 #. CT0 xomofl500 w 8 1 8 6 #.

3

ai @ wvu: March 2014

http://ai-at-wvu.blogspot.com/2014_03_01_archive.html

Modeling Intelligence Lab ("the MILL"). Tuesday, March 25, 2014. Update 5/13 B: Looks about the same with heaven rankings. Closeness definition: (ranges from 0: bad to 1: good). Graphs; Beware: they alternate Heaven Hell, pD pF. Update 5/13: Looking good against default parameters. Unoptimized" learners are the following default parameters:. SKL Gaussian Bayes: {}. Alpha': 1.0, 'fit prior': True}. Binarize': 0.5, 'alpha': 1.0, 'fit prior': True}. How do you feel about non-dominated sort on pD, 1-pF?

4

ai @ wvu: New Param Ranges

http://ai-at-wvu.blogspot.com/2014/11/new-param-ranges.html

Modeling Intelligence Lab ("the MILL"). Thursday, November 20, 2014. Table of Rank Sums Across All Datasets (Random Forest). 66 : Default Cur - Cur. 41 : Tuned Prev - Cur. 28 : Tuned Cur - Cur. 0 : Default Prev - Cur. Table of Rank Sums Across All Datasets (Logistic Regression). 66 : Default Cur - Cur. 41 : Tuned Prev - Cur. 28 : Tuned Cur - Cur. 0 : Default Prev - Cur. Table of Rank Sums Across All Datasets (Bernoulli NB). 41 : Tuned Cur - Cur. 28 : Tuned Prev - Cur. 20 : Default Cur - Cur.

5

ai @ wvu: October 2014

http://ai-at-wvu.blogspot.com/2014_10_01_archive.html

Modeling Intelligence Lab ("the MILL"). Wednesday, October 8, 2014. XOMO PAPER v2.0. Techniques -effort -months -defects -risks # Base Line xomoal500 m 3 4 1 63 # CT0* xomoal500 m 2 4 0 63 # CT0 xomoal500 m 1 4 0. 23 # CT1* xomoal500 m 2 3 3 24 # CT1 xomoal500 m 1 3 3 23 # NSGA xomoal500 m 7 44 1 0 # Base Line xomoal500 q 1 0 6 11 # CT0* xomoal500 q 2 0 6 15 # CT0 xomoal500 q 3 1. 62 49 # CT1* xomoal500 w 42 3 65 44 # CT1 xomoal500 w 40. 17 # CT1* xomofl500 m 3 3 4 3 # CT1 xomofl500 m 4 3 6 4. 11 # CT1* ...

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kocaguneli.com kocaguneli.com

Ekrem Kocaguneli: How big is your data?

http://www.kocaguneli.com/2013/01/how-big-is-your-data.html

Ekrem Kocaguneli: Software Development Engineer II at Microsoft. Thursday, January 24, 2013. How big is your data? I think one of the best ways to describe the popularity concept is to start with a toy example. Assume that we have a data set of N instances (rows) and D features (columns). Let's say that we found out the k-. Many nearest neighbors of every instance. For the sake of this example let's fix k=1,. How to of QUICK. To begin with, the concept of popularity and the QUICK algorithm (developed on ...

kocaguneli.com kocaguneli.com

Ekrem Kocaguneli: Distributed Development Considered Harmful?

http://www.kocaguneli.com/2013/07/distributed-development-considered.html

Ekrem Kocaguneli: Software Development Engineer II at Microsoft. Monday, July 15, 2013. Distributed Development Considered Harmful? Distributed development is a good use of contemporary collaboration technology and the universal talent pool of software engineers. However, it also comes with various possible challenges. Our third rule is to “reflect” on results to avoid confusing practitioners with an arcane mathematical analysis. For example, on reflection, we found that the effect size o...Our conclusio...

kocaguneli.com kocaguneli.com

Ekrem Kocaguneli: Publications

http://www.kocaguneli.com/p/publications.html

Ekrem Kocaguneli: Software Development Engineer II at Microsoft. E Kocaguneli, T. Menzies, E. Mendes, “Transfer Learning in Effort Estimation”. Empirical Software Engineering Journal, vol. 20, no. 3, pp. 813-843, 2015. E Kocaguneli, T. Menzies, “Software Effort Models Should be Assessed via Leave-one-out Validation”. Journal of Systems and Software, vol. 86, no. 7, pp. 1879-1890, 2013. E Kocaguneli, T. Menzies, J. Keung, “Kernel Methods for Software Effort Estimation”. Software Quality Professional, 2011.

kocaguneli.com kocaguneli.com

Ekrem Kocaguneli: It has been some time... Or what I have been up to...

http://www.kocaguneli.com/2015/06/it-has-been-some-time-or-what-i-have.html

Ekrem Kocaguneli: Software Development Engineer II at Microsoft. Wednesday, June 10, 2015. It has been some time. Or what I have been up to. Outside of work, most of my time was dedicated to book projects that I was lucky enough to be involved in. The first book that I had the chance to co-author, with amazing collaborators Tim Menzies. Fayola Peters and Burak Turhan. Is called "Sharing Data and Models in Software Engineering" (here is the Amazon link: http:/ bit.ly/shrngDtMdls. That led to our chapter in.

kocaguneli.com kocaguneli.com

Ekrem Kocaguneli: About PhD Defense

http://www.kocaguneli.com/2012/11/about-phd-defense.html

Ekrem Kocaguneli: Software Development Engineer II at Microsoft. Tuesday, November 20, 2012. Two weeks ago, I successfully defended my PhD thesis: "A Principled Methodology: A Dozen Principles of Software Effort Estimation." I have been willing to write this post right after my PhD defense, but maybe it is better that some time has passed and I had more time to think about it. I will not necessarily write about the details of my PhD thesis in this post (but in case you want to read the details, here.

kocaguneli.com kocaguneli.com

Ekrem Kocaguneli: Resume

http://www.kocaguneli.com/p/resume.html

Ekrem Kocaguneli: Software Development Engineer II at Microsoft. West Virginia University, (2010 Current), Morgantown/USA, PhD (ABD) in Computer Science, 3.85/4.00. Bogazici University, (2008 - 2010), Istanbul/Turkey, MSc in Computer Engineering, 3.65/4.00, Thesis: “Better Methods for Configuring Case-based Reasoning Systems”. Bogazici University, (2003 - 2008), Istanbul/Turkey, BSc in Computer Engineering, 3.23/4.00. University of Bonn, (2006 - 2007), Bonn/Germany, Exchange Student. Inveon Software Deve...

kocaguneli.com kocaguneli.com

Ekrem Kocaguneli: In the market for a non-academic job after a CS (or possibly an EE) PhD?

http://www.kocaguneli.com/2013/05/in-market-for-non-academic-job-after-cs.html

Ekrem Kocaguneli: Software Development Engineer II at Microsoft. Tuesday, May 7, 2013. In the market for a non-academic job after a CS (or possibly an EE) PhD? Anyone reading this title may immediately say: "Why on earth are you looking for a non-academic job, if you enrolled to a PhD program? My answer is simply no, it really is "not" a must, but it doesn't hurt. Also, if you want even more examples and challenges, here is a link to a set of 50 questions prepared by Xiu Zichuan: http:/ goo.gl/yrKVd.

kocaguneli.com kocaguneli.com

Ekrem Kocaguneli: Ground Zero: When do I have perfect data?

http://www.kocaguneli.com/2012/10/ground-zero-when-do-i-have-perfect-data.html

Ekrem Kocaguneli: Software Development Engineer II at Microsoft. Monday, October 1, 2012. Ground Zero: When do I have perfect data? Probably anyone who has dealt with real-world data has a grin on his face after reading this title. Most likely, because of the fact that they know the answer is. well, never. Unfortunately there is no formula or silver-bullet answer to perfect data. This is mainly due to the fact that reaching the right data is an interplay of various different factors such as:. Principle #...

kocaguneli.com kocaguneli.com

Ekrem Kocaguneli: Looking for something on transfer learning (a.k.a. cross-company learning) ?

http://www.kocaguneli.com/2013/07/looking-for-something-on-transfer.html

Ekrem Kocaguneli: Software Development Engineer II at Microsoft. Thursday, July 18, 2013. Looking for something on transfer learning (a.k.a. cross-company learning)? In the machine learning community. The basic idea of transfer learning is being able to learn when the source and the target domains are different or when the source and the target tasks are different. How to handle insufficient number of labeled instances? In this position paper, we investigate synergies between different learning methods (...

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ai @ wvu

Modeling Intelligence Lab ("the MILL"). Thursday, November 20, 2014. Table of Rank Sums Across All Datasets (Random Forest). 66 : Default Cur - Cur. 41 : Tuned Prev - Cur. 28 : Tuned Cur - Cur. 0 : Default Prev - Cur. Table of Rank Sums Across All Datasets (Logistic Regression). 66 : Default Cur - Cur. 41 : Tuned Prev - Cur. 28 : Tuned Cur - Cur. 0 : Default Prev - Cur. Table of Rank Sums Across All Datasets (Bernoulli NB). 41 : Tuned Cur - Cur. 28 : Tuned Prev - Cur. 20 : Default Cur - Cur. 23 # CT1* xo...

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