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.
標(biāo)簽： experimental generation advances enormous
上傳時(shí)間： 2016-10-23
上傳用戶：wkchong
一個(gè)神經(jīng)網(wǎng)絡(luò)原型代碼，針對(duì)的例子為《Perceptron LEARNing》 (Russell & Norvig， 第742頁(yè))
標(biāo)簽： 神經(jīng)網(wǎng)絡(luò) 原型 代碼
上傳時(shí)間： 2016-11-06
上傳用戶：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
標(biāo)簽： package multi-label includes networks
上傳時(shí)間： 2013-12-05
上傳用戶：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.
標(biāo)簽： 64257 algorithms decision pattern
上傳時(shí)間： 2014-01-12
上傳用戶：wangdean1101
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.
標(biāo)簽： multimedia Semantic analysis research
上傳時(shí)間： 2016-11-24
上傳用戶：蟲(chóng)蟲(chóng)蟲(chóng)蟲(chóng)蟲(chóng)蟲(chóng)
另一本介紹貝葉斯網(wǎng)絡(luò)的經(jīng)典教材，可以與LEARNing Bayesian Networks配合使用，相得益彰。
標(biāo)簽： 貝葉斯 網(wǎng)絡(luò) 教材
上傳時(shí)間： 2014-01-05
上傳用戶：電子世界
％For the following 2-class problem determine the decision boundaries ％obtained by LMS and perceptron LEARNing laws.
標(biāo)簽： boundaries the following determine
上傳時(shí)間： 2016-11-26
上傳用戶：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
標(biāo)簽： corresponding time-series predicted analysis
上傳時(shí)間： 2016-11-27
上傳用戶：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
標(biāo)簽： implementation variational introduced framework
上傳時(shí)間： 2016-12-16
上傳用戶：eclipse
* Lightweight backpropagation neural network. * This a lightweight library implementating a neural network for use * in C and C++ programs. It is intended for use in applications that * just happen to need a simply neural network and do not want to use * needlessly complex neural network libraries. It features multilayer * feedforward perceptron neural networks, sigmoidal activation function * with bias, backpropagation training with settable LEARNing rate and * momentum, and backpropagation training in batches.
標(biāo)簽： backpropagation implementating Lightweight lightweight
上傳時(shí)間： 2013-12-27
上傳用戶：清風(fēng)冷雨
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