The module LSQ is for unconstrained linear least-squares fitting. It is
based upon Applied Statistics algorithm AS 274 (see comments at the start
of the module). A planar-rotation algorithm is used to update the QR-
factorization. This makes it suitable for updating regressions as more
data become available. The module contains a test for singularities which
is simpler and quicker than calculating the singular-value decomposition.
An important feature of the algorithm is that it does not square the condition
number. The matrix X X is not formed. Hence it is suitable for ill-
conditioned problems, such as fitting polynomials.
By taking advantage of the MODULE facility, it has been possible to remove
many of the arguments to routines. Apart from the new function VARPRD,
and a back-substitution routine BKSUB2 which it calls, the routines behave
as in AS 274.
The EM algorithm is short for Expectation-Maximization algorithm. It is based on an iterative optimization of the centers and widths of the kernels. The aim is to optimize the likelihood that the given data points are generated by a mixture of Gaussians. The numbers next to the Gaussians give the relative importance (amplitude) of each component.
A Java Framework for connecting to and exchanging data from GPS units to J2ME Mobile Devices. Serial and Bluetooth connections to GPS devices. Support for NMEA-0183 GPS Standard. An Observer-Design Pattern makes the library extendable. Based on GPSlib4j.
A Java web application, based on Struts and Hibernate, that serves as an online running log. Users may enter information about workouts, and can track historical performance and data using web-based charts.
"poco" (Spanish & Italian for "little") OLAP provides a web-based, crosstab reporting tool for your datawarehouse. While it s not an OLAP server or full fledged data mining solution, pocOLAP makes your data easy to use and understand ... for free!
Feature selection is a preprocessing technique frequently used in data mining and machine learning tasks. It can reduce dimensionality, remove irrelevant data, increase learning accuracy, and improve results comprehensibility. FCBF is a fast correlation-based filter algorithm designed for high-dimensional data and has been shown effective in removing both irrelevant features and redundant features
The need for accurate monitoring and analysis of sequential data arises in many scientic, industrial
and nancial problems. Although the Kalman lter is effective in the linear-Gaussian
case, new methods of dealing with sequential data are required with non-standard models.
Recently, there has been renewed interest in simulation-based techniques. The basic idea behind
these techniques is that the current state of knowledge is encapsulated in a representative
sample from the appropriate posterior distribution. As time goes on, the sample evolves and
adapts recursively in accordance with newly acquired data. We give a critical review of recent
developments, by reference to oil well monitoring, ion channel monitoring and tracking
problems, and propose some alternative algorithms that avoid the weaknesses of the current
methods.
today bought a book, reflected good to upload source code package. 1. Based on the struts of customer information management system 2. Struts-based personnel management system 3. Office log system 4. E-government management system 5. Food industry Invoicing System 6 SMS Data Acquisition System
SDP, Service Delivery Platform, is more for telecom operators who want to manage the Data Service better delivered to the end device users by bridging with back-end content providers. Operators rely on the content provider to create & distribute data content to different types of devices. This is different from the open world in the internet communication. Operators must control who can access what content based on his rate plans. Also, based the content access results, the process will be recorded as the transaction records based on which billing statements can be generated to collected the money and shared by operators and content providers. I am working on the conceptual architecture level and the real implementation is very complicated due to too many types of service from different content providers to different types of devices based on the different types of the rate plans.
SDP, Service Delivery Platform, is more for telecom operators who want to manage the Data Service better delivered to the end device users by bridging with back-end content providers. Operators rely on the content provider to create & distribute data content to different types of devices. This is different from the open world in the internet communication. Operators must control who can access what content based on his rate plans. Also, based the content access results, the process will be recorded as the transaction records based on which billing statements can be generated to collected the money and shared by operators and content providers. I am working on the conceptual architecture level and the real implementation is very complicated due to too many types of service from different content providers to different types of devices based on the different types of the rate plans.
