Personalized User Model LLP v. Google Inc.

Filing 137

REDACTED VERSION of 133 Declaration of Jennifer D. Bennett in Support of Plaintiff Personalized User Model, L.L.P.'s Responsive Claim Construction Brief by Personalized User Model LLP. (Attachments: # 1 Exhibit 1-12)(Tigan, Jeremy)

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Personalized User Model LLP v. Google Inc. Doc. 137 Att. 1 EXHIBIT 1 Dockets.Justia.com Official Google Blog: Personalized Search for everyone Search powered by Site Feed Personalized Search for everyone 12/04/2009 03:01:00 PM Today we're helping people get better search results by extending Personalized Search to signed-out users worldwide, and in more than forty languages. Now when you search using Google, we will be able to better provide you with the most relevant results possible. For example, since I always search for [recipes] and often click on results from epicurious.com, Google might rank epicurious.com higher on the results page the next time I look for recipes. Other times, when I'm looking for news about Cornell University's sports teams, I search for [big red]. Because I frequently click on www.cornellbigred.com, Google might show me this result first, instead of the Big Red soda company or others. Previously, we only offered Personalized Search for signed-in users, and only when they had Web History enabled on their Google Accounts. What we're doing today is expanding Personalized Search so that we can provide it to signed-out users as well. This addition enables us to customize search results for you based upon 180 days of search activity linked to an anonymous cookie in your browser. It's completely separate from your Google Account and Web History (which are only available to signed-in users). You'll know when we customize results because a "View customizations" link will appear on the top right of the search results page. Clicking the link will let you see how we've customized your results and also let you turn off this type of customization. Check out our help center for more details on personalized search, how we customize results and how you can turn off personalization. Learn more by watching our video: Archives Archives More Blogs from Google Visit our directory for more information about Google blogs. Sign up to get our posts via email. No more than one message per day. Subscribe Delivered by FeedBurner Recent posts from our blogs The art of search results The Official Google Blog Posted by Bryan Horling, Software Engineer and Matthew Kulick, Product Manager Google Files Privacy Permalink Comments http://googleblog.blogspot.com/2009/12/personalized-search-for-everyone.html (1 of 9)4/19/2010 2:59:03 PM Official Google Blog: Personalized Search for everyone Share: Google Public Policy Blog Labels: search Google's Innovation Factory (and how testing adapts) Google Testing Blog Links to this post About Google Code Jam 2010 When Google Narrows Your Search What is Google Personalized Search Blurring the Line: Non-Line Keres�marketing helyzet 2010 � SEMPO adatok Driven Analytics The Rise of Universal Paid Search Google Retail Blog Google Student Blog Google Uses Personal Data to Tailor 20% of Searches Thank you Google for this precious lesson about privacy Dumb SEO is DEAD g**gl*s personalisierte Suche nun auch f�r ausgeloggte Benutzer Google Buzz: Convenience or the ultimate data tracking tool? Google Search Gets Personal Jag �r tillbaka i SEO-v�rlden Is your SEO Really Optimized? Why did Google lumber us with Personalised Search? Newest Google blogs DoubleClick for Publishers API Blog Google Translate Blog Google Wave Blog Google New Zealand Blog Data Liberation Blog Is Google making us less rational? How To Fight Personalization -- Is It Possible? Historique et personnalisation des r�sultats sur Google Personalized Results and Paid Search Are Not A Match Keeping Score: 10 Predictions for 2009 -- How'd Bruce Do? Google-Blues: Wo bleibt der Long-Tail-Effekt? Google in Review: The Search Giant in 2009 Personalized Search And Your SEO Efforts Labels accessibility (27) acquisition (11) ads (69) Africa (2) apps (298) Personalized Search And Your SEO Efforts Reader Rescue: Do I have to disable Google personalization ... http://googleblog.blogspot.com/2009/12/personalized-search-for-everyone.html (2 of 9)4/19/2010 2:59:03 PM Official Google Blog: Personalized Search for everyone Q and A: Do I have to disable Google personalization settings to ... Q and A: Do I have to disable Google personalization settings to ... Is Google Watching You? Personnalisation des r�sultats Google, pensez � d�sactiver l ... Don't Trust that Google Ranking: The New Personalized Search Blindfold Google Personalizes Search Results for Everyone Google's Changes and What You Need To Do On Today on Ecom Experts Personalized search by default 10 News Media Content Trends to Watch in 2010 10 News Media Content Trends to Watch in 2010 | Vadim Lavrusik ... 10 News Media Content Trends to Watch in 2010 Is the only protectionist ORM strategy one that embraces optimised ... Advent Calendar � Time to reflect: social media and world events Google stories How Personalized Search Changes SEO (and Doesn't) How Personalized Search Changes SEO (and Doesn't) El apabullante -y escalofriante- ritmo de Google Google Takes it Personal AdWords-Shortcuts: News im �berblick KW 51 "SEO"......SEO 10&SEO ... Your Google results are about to get weirder Official Google Blog: Personalized Search for everyone Big Changes at Google Can SEO Exist Beyond Google Personalization? Can SEO Exist Beyond Google Personalization? Google now defaults to Personalized Search for everyone Will the middle be harder to find?... As tenor and temperature of ... Google Is "Personalizing" Searches Here's How To Get Real Ranking The Weekly Insider 12-7-09 to 12-11-09 Google vs SEO � " " Personalized search is now Google's default Top 5 Reasons for Ongoing SEO Services Google : La recherche personnalis�e devient universelle Real Time Search Has Arrived! Real Time Search Has Arrived! Google blir personligt som standard Google's SEO Bombshell - Personalized Search is Here All You Need to Know About Google's New Feature Updates ! 10&SEO ... Golpe al SEO: b�squeda personalizada por defecto para todos Personalisierung von Suchergebnissen - f�nf Probleme! Google Personalised Search Google : r�sultats personnalis�s pour tout le monde... So what ? Contextualised Relevance New Google Tools Promising for Affiliates Official Google Blog: Personalized Search for everyone Mobile More... Google Personalizes Search Results for Everyone Google: Chrome un paplasinjumi, k ar daudz kas cits Google blir personlig - information och instruktioner Google ut�kar personaliserat s�k till alla Wijziging Google Personalized Search � de gevolgen �� "�� " �� -, Google's Latest Releases Part 1: Real-time Search Google Extends Personalized Search What We're Reading Ars Technica Ask.com Blog Google Guide Google OS John Battelle's Searchblog Marketing Pilgrim MSN Search Weblog O'Reilly Radar Pandia Search World Philipp Lenssen's Google Blogoscoped Personalized Search: The End for Some, but a Breakthrough for Most ... Google's Universal Customization Has SEO Implications Google Personalised Search Personalized search for all at Google Personalizar� Google los Resultados de los Dispositivos? M�s personalizaci�n, nuevos desaf�os Read/Write Web Search Engine Journal Search Engine Land Search Engine Roundtable Search Engine Watch Blog Slashdot - Google Techdirt The Launch Pad - X PRIZE Traffick WebmasterWorld Yahoo Search Blog Endgame/New Game: Google Search Moves Focus on The Moment of ... How will Google Personalized Search affect affiliate marketing? Don't Be Evil Google announcements - personal search, visual search, realtime ... All Google Searches Are Now Automatically 'Personalized' GOOGLE: orice utilizator este urmarit in numele unui serviciu mai bun Popeye, Goggles und mehr: Google dreht auf Personalized Search Opacity La ricerca personalizzata di Google e le possibili ricadute nel ... 10 viktiga konsekvenser med personliga s�kresultat SEO Google Google is watching you! PsychoCoder's Realm - Google has crossed the line http://googleblog.blogspot.com/2009/12/personalized-search-for-everyone.html (5 of 9)4/19/2010 2:59:03 PM Official Google Blog: Personalized Search for everyone Blogs By Googlers Google Presents Personalized Search for Everyone 20-Something Finance Abe Tries Again Alon Chen's Diary-�� Beyond Satire Bikin' my Bloggin' Bladam 2.0 Bolinfest Changeblog Catspaw's Guide to the Inevitably Insane Google and the Ostrich Effect Google personalizar� las b�squedas aunque el internauta no lo pida Google, how far is too far? in Corner Cubicle Google har blivit personligt � alldeles otroligt personligt The Implication of Google Personalized Results Personalizacja wynik�w wyszukiwania i usage data w SEO Google: Recherche en Temps R�el et R�sultats Personnalis�s Code 2D, recherche temps r�el, recherche visuelle et personnalis�e... Google Offers 'Personalized Search' To Everyone Google � Inflacija novotarija. Fokus: Social Search From "Personalized Search for everyone" to "Personalized ... A Final Nail in the Coffin of "Google Ground Truth"? Google offers personalised search even when not logged into Google The new search engine: the impact of personalized search results Eric Schmidt, The Wall Street Journal and Personalized Search Expanded Google Personalized Search...What Does It Mean for SEO? Google personalizar� las b�squedas aunque el internauta no lo pida Google Personalized Search Personalisierte Suche f�r jedermann Google personalizzato di default Google, The Big Brother Google, The Big Brother Confessions of a Digital Packrat Damon Kohler Donal Mountain's notes Dr. Razavi's Good-to-KnowInfo Ego Food Erica's Joys Germart Grokster Gyula Simonyi: smart design iBanjo It Has Come to My Attention Jason Morrison.net Jens Meiert JR Says Kraneland Lorem Ipsum Google: I See Results You Don't See...? Google g�n�ralise la personnalisation des r�sultats Google personalizar� las b�squedas aunque el internauta no lo pida TechCrunch: Lazyfeed's New Realtime Interface Tips Into ... Google Will "Personalize" Your Search When You're Not Logged In Google personalises everyone's search results http://googleblog.blogspot.com/2009/12/personalized-search-for-everyone.html (6 of 9)4/19/2010 2:59:03 PM Official Google Blog: Personalized Search for everyone Lazyfeed's New Realtime Interface Tips Into Information Overload The SEO guide to Google personalized search Google extends personalised search to all users Can You "Rank" in Google if Everyone Has Different Search Results? Google Search Gets Personal With Everyone Google / Google extends personalised search to all users El problema de la personalizaci�n inevitable Problems with Google's personalized search Personalized search at Google Positionnement Google : la personnalisation va t-elle changer la ... Google lance la recherche personnalis�e pour tous Google Extended Personalized Search for Everyone Getting To Know You, Getting To Know All About You Google Tracks & Personalizes Even When You're Signed-Out Google and its new personalized search The Importance of Social Networking Just Grew Exponentially Overnight Google Altered SERPS For EVERYONE Now �� 12/4/2009 Google Roundup Google Personalised Search Google's personalized search now works even when you're not signed in Fluch oder Segen? Personalisierte Suchergebnisse bei Google Google personalisiert alle Suchanfragen Personalised Search Google da a cada uno un resultado Google Caffeine Update Gepersonaliseerde zoekresultaten Google zonder in te loggen Official Google Blog: Personalized Search for everyone Official Google Blog: Personalized Search for everyone Personalized Search for everyone (Bryan Horling/The Official ... Personalized Search and SEO Create a Link Newer Post Home Older Post Copyright � 2009 Google Inc. All rights reserved. Privacy Policy | Terms of Service http://googleblog.blogspot.com/2009/12/personalized-search-for-everyone.html (9 of 9)4/19/2010 2:59:03 PM EXHIBIT 2 Google Ads Preferences Page 1 of 2 Ads Preferences Make the ads you see on the web more interesting Many websites, such as news sites and blogs, partner with us to show ads on their sites. To see ads that are more related to your interests, edit the interest categories below, which are based on sites you have recently visited. Learn more Your interests are associated with an advertising cookie that's stored in your browser. If you don't want us to store your interests, you can opt out below. Watch our video: Ads Preferences explained Ads Preferences affect ads that Google shows on other websites. 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Your cookie Google stores the following information in a cookie to associate your ads preferences with the browser you are currently using: id=2207ad96f9000012|2588783/907846/14781,1092688/350444/14781,22 Visit the Advertising and Privacy page of our Privacy Center to learn more. http://www.google.com/ads/preferences/view?sig=ACi0TChX8kKZB6i158MWsDpr2lvjug... 7/8/2010 Google Ads Preferences Page 2 of 2 Google is a participating member of the Network Advertising Initiative. You can opt out of this cookie as well as other network advertising cookies from the Network Advertising Initiative opt-out page. Your ads preferences only apply in this browser on this computer. They are reset if you delete your browser's cookies. �2009 Google - Home- Privacy Policy http://www.google.com/ads/preferences/view?sig=ACi0TChX8kKZB6i158MWsDpr2lvjug... 7/8/2010 EXHIBIT 3 How it works - Google News Help Sign in Search Help News Help Help topics Recommended articles Google News > Help articles > Features > Recommended Stories > How it works About Google News Recommended Stories: Basics News for Mobile Devices: Supported Phones and Devices How it Works: Languages and regions Content in Google News: Feedback on articles Content in Google News: Weather information Features Hide Try Google's new browser. Browse the web faster, safer, and more easily with Google Chrome. Help forum Share Comment Print Recommended Stories: How it works Help for Publishers About Google News When you sign in to personalized News and keep Web History enabled, you allow Google to track and save your news selections. Then, Google News can automatically recommend relevant stories just for you by using smart algorithms that analyze your selections. Learn from other Google users News Blog Find answers, ask questions, and share your expertise with others in the Google News Help Forum. Contacting Support The News algorithms compare your tastes to the aggregate tastes of other groups of Google News users. Simply put, we recommend news stories to you that have been read by many other users who've also read similar stories as you in the past. The more you use Google News while you're signed in to your Google Account, the better your recommendations will become over time. Learn more about Web History. Note: we can't provide recommended news for you if you don't sign in to your Google Account, or if you turn off the Web History component of personalized Google News. Help for Publishers Was this information helpful? No Yes Are you a news publisher? We encourage you to visit our Publisher Help Center for help with your site. Here, you'll find our most comprehensive, up-to-date information for publishers. Recently, on the official News Blog http://www.google.com/support/news/bin/answer.py?hl=en&answer=82464 (1 of 2)7/16/2010 1:16:05 PM How it works - Google News Help 7/15/2010 Posted by Chris Beckmann, Product Manager Two weeks ago we gave the Google News homepage a new look and feel with enhanced customization, discovery and sharing. This Read more Suggest A Feature Got a feature suggestion for Google News? Let us know Google News - Contacting Us - Help with other Google products - Change Language: English (US) �2010 Google - Google Home - Privacy Policy - Terms of Service http://www.google.com/support/news/bin/answer.py?hl=en&answer=82464 (2 of 2)7/16/2010 1:16:05 PM EXHIBIT 4 EXHIBIT 5 EXHIBIT 6 EXHIBIT 7 FULLY REDACTED EXHIBIT 8 Machine learning - Wikipedia, the free encyclopedia Page 1 of 7 Machine learning From Wikipedia, the free encyclopedia Machine learning is a scientific discipline that is concerned with the design and development of algorithms that allow computers to evolve behaviors based on empirical data, such as from sensor data or databases. A learner can take advantage of examples (data) to capture characteristics of interest of their unknown underlying probability distribution. Data can be seen as examples that illustrate relations between observed variables. A major focus of machine learning research is to automatically learn to recognize complex patterns and make intelligent decisions based on data; the difficulty lies in the fact that the set of all possible behaviors given all possible inputs is too large to be covered by the set of observed examples (training data). Hence the learner must generalize from the given examples, so as to be able to produce a useful output in new cases. Artificial intelligence is a closely related field, as are probability theory and statistics, data mining, pattern recognition, adaptive control, computational neuroscience and theoretical computer science. Contents 2 1 Definition 3 Generalization 4 Human interaction 5 Algorithm types 6 Theory Approaches 6 6.1 Decision tree learning 6.2 Association rule learning 6.3 Artificial neural networks 6.4 Genetic programming 6.5 Inductive logic programming 6.6 Support vector machines 6.7 Clustering 6.8 Bayesian networks 7 .9 Reinforcement learning 8 Applications 9 Software 1 Journals and conferences 10 See also 11 References 12 Further reading 3 External links D efinition A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P, if its performance at tasks in T, as measured by P, improves with experience E.[1] http://en.wikipedia.org/wiki/Machine_learning 12/9/2010 Machine learning - Wikipedia, the free encyclopedia Page 2 of 7 Generalization The core objective of a learner is to generalize from its experience.[2] The training examples from its experience come from some generally unknown probability distribution and the learner has to extract from them something more general, something about that distribution, that allows it to produce useful answers in new cases. Human interaction Some machine learning systems attempt to eliminate the need for human intuition in data analysis, while others adopt a collaborative approach between human and machine. Human intuition cannot, however, be entirely eliminated, since the system's designer must specify how the data is to be represented and what mechanisms will be used to search for a characterization of the data. Algorithm types Machine learning algorithms are organized into a taxonomy, based on the desired outcome of the algorithm. U Supervised learning generates a function that maps inputs to desired outputs. For example, in a classification problem, the learner approximates a function mapping a vector into classes by looking at input-output examples of the function. S nsupervised learning models a set of inputs, like clustering. Remi-supervised learning combines both labeled and unlabeled examples to generate an appropriate function or classifier. T einforcement learning learns how to act given an observation of the world. Every action has some impact in the environment, and the environment provides feedback in the form of rewards that guides the learning algorithm. Lransduction tries to predict new outputs based on training inputs, training outputs, and test inputs. earning to learn learns its own inductive bias based on previous experience. T heory Main article: Computational learning theory The computational analysis of machine learning algorithms and their performance is a branch of theoretical computer science known as computational learning theory. Because training sets are finite and the future is uncertain, learning theory usually does not yield absolute guarantees of the performance of algorithms. Instead, probabilistic bounds on the performance are quite common. In addition to performance bounds, computational learning theorists study the time complexity and feasibility of learning. In computational learning theory, a computation is considered feasible if it can be done in polynomial time. There are two kinds of time complexity results. Positive results show that a certain class of functions can be learned in polynomial time. Negative results show that certain classes cannot be learned in polynomial time. http://en.wikipedia.org/wiki/Machine_learning 12/9/2010 Machine learning - Wikipedia, the free encyclopedia Page 3 of 7 There are many similarities between machine learning theory and statistics, although they use different terms. Approaches Main article: List of machine learning algorithms Decision tree learning Main article: Decision tree learning Decision tree learning uses a decision tree as a predictive model which maps observations about an item to conclusions about the item's target value. Association rule learning Main article: Association rule learning Association rule learning is a method for discovering interesting relations between variables in large databases. Artificial neural networks Main article: Artificial neural network An artificial neural network (ANN), usually called "neural network" (NN), is a mathematical model or computational model that tries to simulate the structure and/or functional aspects of biological neural networks. It consists of an interconnected group of artificial neurons and processes information using a connectionist approach to computation. Modern neural networks are non-linear statistical data modeling tools. They are usually used to model complex relationships between inputs and outputs or to find patterns in data. Genetic programming Main articles: Genetic programming and Evolutionary computation Genetic programming (GP) is an evolutionary algorithm-based methodology inspired by biological evolution to find computer programs that perform a user-defined task. It is a specialization of genetic algorithms (GA) where each individual is a computer program. It is a machine learning technique used to optimize a population of computer programs according to a fitness landscape determined by a program's ability to perform a given computational task. Inductive logic programming Main article: Inductive logic programming http://en.wikipedia.org/wiki/Machine_learning 12/9/2010 Machine learning - Wikipedia, the free encyclopedia Page 4 of 7 Inductive logic programming (ILP) is an approach to rule learning using logic programming as a uniform representation for examples, background knowledge, and hypotheses. Given an encoding of the known background knowledge and a set of examples represented as a logical database of facts, an ILP system will derive a hypothesized logic program which entails all the positive and none of the negative examples. Support vector machines Main article: Support vector machines Support vector machines (SVMs) are a set of related supervised learning methods used for classification and regression. Given a set of training examples, each marked as belonging to one of two categories, an SVM training algorithm builds a model that predicts whether a new example falls into one category or the other. Clustering Main article: Cluster analysis Cluster analysis or clustering is the assignment of a set of observations into subsets (called clusters) so that observations in the same cluster are similar in some sense. Clustering is a method of unsupervised learning, and a common technique for statistical data analysis. Bayesian networks Main article: Bayesian network A Bayesian network, belief network or directed acyclic graphical model is a probabilistic graphical model that represents a set of random variables and their conditional independencies via a directed acyclic graph (DAG). For example, a Bayesian network could represent the probabilistic relationships between diseases and symptoms. Given symptoms, the network can be used to compute the probabilities of the presence of various diseases. Efficient algorithms exist that perform inference and learning. Reinforcement learning Main article: Reinforcement learning Reinforcement learning is concerned with how an agent ought to take actions in an environment so as to maximize some notion of long-term reward. Reinforcement learning algorithms attempt to find a policy that maps states of the world to the actions the agent ought to take in those states. Reinforcement learning differs from the supervised learning problem in that correct input/output pairs are never presented, nor sub-optimal actions explicitly corrected. http://en.wikipedia.org/wiki/Machine_learning 12/9/2010 Machine learning - Wikipedia, the free encyclopedia Page 5 of 7 Applications Applications for machine learning include machine perception, computer vision, natural language processing, syntactic pattern recognition, search engines, medical diagnosis, bioinformatics, brainmachine interfaces and cheminformatics, detecting credit card fraud, stock market analysis, classifying DNA sequences, speech and handwriting recognition, object recognition in computer vision, game playing, software engineering, adaptive websites, robot locomotion, and structural health monitoring. Machine learning techniques helped win a major software competition: In 2006, the online movie company Netflix held the first "Netflix Prize" competition to find a program to better predict user preferences and beat its existing Netflix movie recommendation system by at least 10%. The AT&T Research Team BellKor won over several other teams with their machine learning program called Pragmatic Chaos. After winning several minor prizes, it won the 2009 grand prize competition for $1 million.[3] Software RapidMiner, KNIME, Weka, ODM, Shogun toolbox and Orange are software suites containing a variety of machine learning algorithms. Journals and conferences J Machine Learning (journal) N urnal of Machine Learning Research o I eural Computation (journal) N ternational Conference on Machine Learning (ICML) (conference) n L eural Information Processing Systems (NIPS) (conference) ist of upcoming conferences in Machine Learning and Artificial Intelligence (http://sites.google.com/site/fawadsyed/upcoming-conferences) (conference) S ee also D Computational intelligence E ata mining I xplanation-based learning mportant publications in machine learning P ulti-label classification Pattern recognition redictive analytics M R eferences 1. ^ Tom M. Mitchell (1997) Machine Learning p.2 2. ^ Christopher M. Bishop (2006) Pattern Recognition and Machine Learning, Springer ISBN 0-387-31073-8. 3. ^ "BelKor Home Page" (http://www2.research.att.com/~volinsky/netflix/) research.att.com http://en.wikipedia.org/wiki/Machine_learning 12/9/2010 Machine learning - Wikipedia, the free encyclopedia age 6 of 7 Further reading E Sergios Theodoridis, Konstantinos Koutroumbas (2009) "Pattern Recognition", 4th Edition, Academic Press, ISBN 978-1-59749-272-0. Bthem Alpaydin (2004) Introduction to Machine Learning (Adaptive Computation and Machine Learning), MIT Press, ISBN 0262012111 T ing Liu (2007), Web Data Mining: Exploring Hyperlinks, Contents and Usage Data (http://www.cs.uic.edu/~liub/WebMiningBook.html) . Springer, ISBN 3540378812 Roby Segaran, Programming Collective Intelligence, O'Reilly ISBN 0-596-52932-5 Ray Solomonoff, "An Inductive Inference Machine (http://world.std.com/~rjs/indinf56.pdf) " A privately circulated report from the 1956 Dartmouth Summer Research Conference on AI. Ray Solomonoff, An Inductive Inference Machine, IRE Convention Record, Section on Information Theory, Part 2, pp., 56-62, 1957. Ryszard S. Michalski, Jaime G. Carbonell, Tom M. Mitchell (1983), Machine Learning: An Artificial Intelligence Approach, Tioga Publishing Company, ISBN 0-935382-05-4. Yyszard S. Michalski, Jaime G. Carbonell, Tom M. Mitchell (1986), Machine Learning: An Artificial Intelligence Approach, Volume II, Morgan Kaufmann, ISBN 0-934613-00-1. R ves Kodratoff, Ryszard S. Michalski (1990), Machine Learning: An Artificial Intelligence Approach, Volume III, Morgan Kaufmann, ISBN 1-55860-119-8. Byszard S. Michalski, George Tecuci (1994), Machine Learning: A Multistrategy Approach, Volume IV, Morgan Kaufmann, ISBN 1-55860-251-8. Bhagat, P.M. (2005). Pattern Recognition in Industry, Elsevier. ISBN 0-08-044538-1. Rishop, C.M. (1995). Neural Networks for Pattern Recognition, Oxford University Press. ISBN 0-19-853864 -2. Hichard O. Duda, Peter E. Hart, David G. Stork (2001) Pattern classification (2nd edition), Wiley, New York, ISBN 0-471-05669-3. Kuang T.-M., Kecman V., Kopriva I. (2006), Kernel Based Algorithms for Mining Huge Data Sets, Supervised, Semi-supervised, and Unsupervised Learning (http://learning-from-data.com) , Springer-Verlag, Berlin, Heidelberg, 260 pp. 96 illus., Hardcover, ISBN 3-540-31681-7. MECMAN Vojislav (2001), Learning and Soft Computing, Support Vector Machines, Neural Networks and Fuzzy Logic Models (http://support-vector.ws) , The MIT Press, Cambridge, MA, 608 pp., 268 illus., ISBN 0 -262-11255-8. MacKay, D.J.C. (2003). Information Theory, Inference, and Learning Algorithms (http://www.inference.phy.cam.ac.uk/mackay/itila/) , Cambridge University Press. ISBN 0-521-64298-1. I itchell, T. (1997). Machine Learning, McGraw Hill. ISBN 0-07-042807-7. S n H. Witten and Eibe Frank Data Mining: Practical machine learning tools and techniques Morgan a Kaufmann ISBN 0-12-088407-0. M olom Weiss and Casimir Kulikowski (1991). Computer Systems That Learn, Morgan Kaufmann. ISBN 1h 55860-065-5. T ierswa, Ingo and Wurst, Michael and Klinkenberg, Ralf and Scholz, Martin and Euler, Timm: YALE: Rapid Prototyping for Complex Data Mining Tasks, in Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-06), 2006. Vrevor Hastie, Robert Tibshirani and Jerome Friedman (2001). The Elements of Statistical Learning (http://www-stat.stanford.edu/~tibs/ElemStatLearn/) , Springer. ISBN 0387952845. ladimir Vapnik (1998). Statistical Learning Theory. Wiley-Interscience, ISBN 0471030031. E xternal links Ruby implementations of several machine learning algorithms (http://ai4r.rubyforge.org) A T ndrew Ng's Stanford lectures and course materials (http://see.stanford.edu/see/courseinfo.aspx? coll=348ca38a-3a6d-4052-937d-cb017338d7b1) I he Encyclopedia of Computational Intelligence (http://scholarpedia.org/article/Encyclopedia_of_Computational_Intelligence) nternational Machine Learning Society (http://machinelearning.org/) P http://en.wikipedia.org/wiki/Machine_learning 12/9/2010 Machine learning - Wikipedia, the free encyclopedia age 7 of 7 R M Kmining List of machine learning, data mining and KDD scientific conferences (http://kmining.com/info_conferences.html) Machine Learning Open Source Software (http://mloss.org/about/) O achine Learning Video Lectures (http://videolectures.net/Top/Computer_Science/Machine_Learning/) T pen Source Artificial Learning Software (http://salproject.codeplex.com) Rhe Computational Intelligence and Machine Learning Virtual Community (http://cimlcommunity.org/) MMachine Learning Task View (http://cran.r-project.org/web/views/MachineLearning.html) achine Learning Links and Resources (http://www.reddit.com/r/MachineLearning/) etrieved from "http://en.wikipedia.org/wiki/Machine_learning" Categories: Learning in computer vision | Machine learning | Learning | Cybernetics This page was last modified on 25 November 2010 at 07:26. ext is available under the Creative Commons Attribution-ShareAlike License; additional terms P may apply. See Terms of Use for details. Wikipedia� is a registered trademark of the Wikimedia Foundation, Inc., a non-profit organization. http://en.wikipedia.org/wiki/Machine_learning 12/9/2010 EXHIBIT 9 EXHIBIT 10 EXHIBIT 11 FULLY REDACTED EXHIBIT 12

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