Recent advances in experimental methods have resulted in the generation of enormous volumes of data across the life sciences. Hence clustering and classification techniques that were once predominantly the domain of ecologists are now being used more widely. This book provides an overview of these important data analysis methods, from long-established statistical methods to more recent machine learning techniques. It aims to provide a framework that will enable the reader to recognise the assumptions and constraints that are implicit in all such techniques. Important generic issues are discussed first and then the major families of algorithms are described. Throughout the focus is on explanation and understanding and readers are directed to other resources that provide additional mathematical rigour when it is required. Examples taken from across the whole of biology, including bioinformatics, are provided throughout the book to illustrate the key concepts and each technique’s potential.
标签： experimental generation advances enormous
上传时间： 2016-10-23
上传用户：wkchong
一个神经网络原型代码，针对的例子为《Perceptron Learning》 (Russell & Norvig， 第742页)
上传时间： 2016-11-06
上传用户：康郎
BPMLL is a package for training multi-label BP neural networks. The package includes the MATLAB code of the algorithm BP-MLL, which is designed to deal with multi-label learning. It is in particular useful when a real-world object is associated with multiple labels simultaneously
标签： package multi-label includes networks
上传时间： 2013-12-05
上传用户：xsnjzljj
Many of the pattern fi nding algorithms such as decision tree, classifi cation rules and clustering techniques that are frequently used in data mining have been developed in machine learning research community. Frequent pattern and association rule mining is one of the few excep- tions to this tradition. The introduction of this technique boosted data mining research and its impact is tremendous. The algorithm is quite simple and easy to implement. Experimenting with Apriori-like algorithm is the fi rst thing that data miners try to do.
标签： 64257 algorithms decision pattern
上传时间： 2014-01-12
上传用户：wangdean1101
This demo shows the BER performance of linear, decision feedback (DFE), and maximum likelihood sequence estimation (MLSE) equalizers when operating in a static channel with a deep null. The MLSE equalizer is invoked first with perfect channel knowledge, then with an imperfect, although straightforward, channel estimation algorithm. The BER results are determined through Monte Carlo simulation. The demo shows how to use these equalizers seamlessly across multiple blocks of data, where equalizer state must be maintained between data blocks.
标签： performance likelihood decision feedback
上传时间： 2013-11-25
上传用户：1079836864
Semantic analysis of multimedia content is an on going research area that has gained a lot of attention over the last few years. Additionally, machine learning techniques are widely used for multimedia analysis with great success. This work presents a combined approach to semantic adaptation of neural network classifiers in multimedia framework. It is based on a fuzzy reasoning engine which is able to evaluate the outputs and the confidence levels of the neural network classifier, using a knowledge base. Improved image segmentation results are obtained, which are used for adaptation of the network classifier, further increasing its ability to provide accurate classification of the specific content.
标签： multimedia Semantic analysis research
上传时间： 2016-11-24
上传用户：虫虫虫虫虫虫
另一本介绍贝叶斯网络的经典教材，可以与Learning Bayesian Networks配合使用，相得益彰。
上传时间： 2014-01-05
上传用户：电子世界
％For the following 2-class problem determine the decision boundaries ％obtained by LMS and perceptron learning laws.
标签： boundaries the following determine
上传时间： 2016-11-26
上传用户：guanliya
BP neural network for time series analysis predicted that by entering the corresponding time-series data to predict the future, suitable for beginners on the BP neural network learning
标签： corresponding time-series predicted analysis
上传时间： 2016-11-27
上传用户：cjl42111
The library is a C++/Python implementation of the variational building block framework introduced in our papers. The framework allows easy learning of a wide variety of models using variational Bayesian learning
标签： implementation variational introduced framework
上传时间： 2016-12-16
上传用户：eclipse
