Rapid - I. Converting the Enterprise to an Adaptive Neural Network « Kyield. Those tracking business and financial news may have observed that a little bit of knowledge in the corner office about enterprise architecture, software, and data can cause great harm, including for the occupant, often resulting in a moving van parked under the corner suite of corporate headquarters shortly after headlines on their latest preventable crisis.
Exploitation of ignorance in the board room surrounding enterprise computing has become mastered by some, and is therefore among the greatest of many challenges for emerging technology that has the capacity for significant improvement. The issues surrounding neural networks requires total immersion for extended duration. Since many organizations lack the luxury of time, let’s get to it. Beware the Foreshadow of the Black Swan A recent article by Reuters confirms what is perhaps the worst kept secret in the post printing press era: Many Wall Street executives say wrongdoing is necessary: survey. Anametrix. You want to make better use of data to improve all forms of consumer interactions, from campaign performance and social engagement to web site content and ad planning.
It’s the key to determining whether your marketing decisions lead to success. But your data is trapped in dozens of systems, databases, spreadsheets and applications – both inside and outside your organization. Sound familiar? Unify Your Data Anametrix enables marketers like you to bring together and make sense of all your data, so you can focus your time on the analysis that will drive marketing performance. Turn Data into Insights. Automatic Generation of Neural Network Architecture Using Evolutionary Computation (Advances in Fuzzy Systems: Application and Theory) (9789810231064): E. Vonk, L. C. Jain, R. P. Johnson. An Efficient Density based Improved K- Medoids Clustering algorithm.
An efficient density based improved k-medoids clustering algorithm seminar topic explains about extracting information from raw data using clustering methods.
In order to extract information from raw data kmedoids is the basic method used. Though they are easy to implement but they are many drawbacks in these methods. In order to overcome these drawbacks we propose a density based k-medois clustering method which performs better than DBSCAN in terms of quality. In this paper students can find detailed explanation on advantages of DBSCAN, disadvantages of DBSCAN, evaluation and results, conclusion. For more information on this topic students can download reference material from below link. Computer science and information technology students can find related projects, seminar topics , projects with source code from this site for free download. download An Efficient Density based Improved K- Medoids Clustering algorithm related information from this link. Custom Search. Google Image Result for. Feature selection. In machine learning and statistics, feature selection, also known as variable selection, attribute selection or variable subset selection, is the process of selecting a subset of relevant features for use in model construction.
The central assumption when using a feature selection technique is that the data contains many redundant or irrelevant features. Redundant features are those which provide no more information than the currently selected features, and irrelevant features provide no useful information in any context. Feature selection techniques are a subset of the more general field of feature extraction. Feature extraction creates new features from functions of the original features, whereas feature selection returns a subset of the features. Java Code for Feature Selection. 11Ants Analytics – Advanced Predictive Analytics, Customer Analytics, Predictive Modelling Software. Encog Machine Learning Framework. Bayesian credible intervals in the mainstream medical literature. Bouygues Telecom improves IPTV intelligence. France’s Bouygues Telecom has implemented a multimedia content marketing system from Motorola Mobility to more effectively market its expanding content catalogue.
Motorola’s Media Merchandiser solution enables Bouygues to offer subscriber-personalised bundle marketing, and encourages impulse purchases with targeted offers, pricing and discounts. Capable of marketing and delivering content across managed, over-the-top and mobile networks, the solution also features Digital Rights Management (DRM) license issuance to multiple DRM systems. Bouygues delivers triple-play services via its ‘BBox’ gateway, and while recent IPTV subscriber figures are hard to come by, the company had reached just under 600,000 BBox customers by the end of 2010.
The telco recently revealed plans to introduce a new high-end version of its BBox gateway this month, called ‘BBox Sensation’ – full details here. We welcome reader discussion and request that you please comment using an authentic name. Self-Repairing Bayesian Inference. (This article was originally published at Normal Deviate, and syndicated at StatsBlogs.)
Peter Grunwald gave a talk in the statistics department on Monday. Peter does very interesting work and the material he spoke about is no exception. Here are my recollections from the talk. The summary is this: Peter and John Langford have a very cool example of Bayesian inconsistency, much different than the usual examples of inconsistency. In the talk, Peter explained the inconsistency and then he talked about a way to fix the inconsistency. UFLDL Tutorial - Ufldl. Embedding Strengths in Your Company's DNA. If you want to build a strengths-based organization -- and enjoy the benefits of reduced turnover and greater productivity and profitability -- you can't go halfway.
If you really want everyone in your company talking about their talents, sharing them, and living and breathing the language of strengths, you've got to be all in, or it just won't work.