You've built a vibrant community of Family Guy enthusiasts. The SVD recommendation algorithm took your site to the next level by allowing you to leverage the implicit knowledge of your community. But now you're ready for the next iteration - you are about
Structure and Interpretation of Computer Programs has been MIT's introductory pre-professional computer science subject since 1981. It emphasizes the role of computer languages as vehicles for expressing knowledge and it presents basic principles of abstr
The Rete Algorithm [References] is intended to improve the speed of forward-chained rule systems by limiting the effort required to recompute the conflict set after a rule is fired. Its drawback is that it has high memory space requirements. It takes adva
This is a dictionary of algorithms, algorithmic techniques, data structures, archetypal problems, and related definitions. Algorithms include common functions, such as Ackermann's function. Problems include traveling salesman and Byzantine generals. Some entries have links to implementations and more information. Index pages list entries by area and by type. The two-level index has a total download 1/20 as big as this page.
Evocosm is a set of classes that abstract the fundamental components of an evolutionary algorithm. I'll list the components here with a bit of introduction; you can review the details of the classes by downloading the code archives or by reviewing the online documentation (see the menu at the article's beginning for code and documentation links.) All class documentation was generated from source code comments using doxygen. These docs have not been thoroughly proofread, so they may contain a few typos and minor errors. Self-publishing has taught me the value of a good proofreader... ;} Evolutionary algorithms come in a variety of shapes and flavors, but at their core, they all share certain characteristics: populations that reproduce and mutate through a series of generations, producing future generations based on some measure of fitness. An amazing variety of algorithms can be built on that general framework, which leads me to construct a set of core classes as the basis for future applications.
Fræser is a framework for estimating the parameters of static and dynamic errors-in-variables systems with the opportunity to compare various errors-in-variables parameter estimation algorithms via simulations. It features a graphical user interface and several examples for simultaneously estimating model and noise parameters.
The framework incorporates the following linear and nonlinear estimation methods for static and dynamic systems:
* model parameter estimation for static systems
o Koopmans method
* linear model and noise parameter estimation for dynamic systems
o (extended) instrumental variables method (XIV)
o bias-compensating least-squares method (BCLS)
o Frisch scheme (FS)
o generalized Koopmans-Levin method (GKL)
* nonlinear model parameter estimation for static systems
o nonlinear Koopmans method (NK)
o approximated maximum likelihood method (AML)
* nonlinear model and noise parameter estimation for dynamic systems
o bias-compensated least squares method (BCLS)
o nonlinear Koopmans-Levin method (NKL)
o nonlinear extennonlinear extension to generalized Koopmans-Levin method (NGKL)