a good material for new LEArners to learn java, this is one of Sun s training material.
標(biāo)簽： material LEArners training learn
上傳時間： 2015-04-17
上傳用戶：waitingfy
providing LEArners for programming with C51, and joining in these web,hopefully finding people with C51 programming so that it will can be studied each other.
標(biāo)簽： with programming providing hopefully
上傳時間： 2015-06-22
上傳用戶：tedo811
AdaBoost is an efficient tool in machine learning. It can combine a series of weak LEArners into a strong learner. Besides pattern classification, it also can be applied into feature selection. This document explains the use of AdaBoost.
標(biāo)簽： efficient AdaBoost learning LEArners
上傳時間： 2013-12-06
上傳用戶：小寶愛考拉
This toolbox contains re-implementations of four different multi-instance LEArners, i.e. Diverse Density, Citation-kNN, Iterated-discrim APR, and EM-DD. Ensembles of these single multi-instance LEArners can be built with this toolbox
標(biāo)簽： i.e. re-implementations multi-instance different
上傳時間： 2013-12-19
上傳用戶：haohaoxuexi
it will make it easer for LEArners to learn asm
標(biāo)簽： LEArners easer learn it
上傳時間： 2017-02-06
上傳用戶：banyou
very useful book for new LEArners
標(biāo)簽： LEArners useful very book
上傳時間： 2013-12-01
上傳用戶：hj_18
This is a helpful document for tinyos LEArners
標(biāo)簽： document LEArners helpful tinyos
上傳時間： 2017-04-29
上傳用戶：xieguodong1234
It is very useful java LEArners.
標(biāo)簽： LEArners useful very java
上傳時間： 2017-07-20
上傳用戶：cuiyashuo
Description: FASBIR(Filtered Attribute Subspace based Bagging with Injected Randomness) is a variant of Bagging algorithm, whose purpose is to improve accuracy of local LEArners, such as kNN, through multi-model perturbing ensemble. Reference: Z.-H. Zhou and Y. Yu. Ensembling local LEArners through multimodal perturbation. IEEE Transactions on Systems, Man, and Cybernetics - Part B: Cybernetics, 2005, vol.35, no.4, pp.725-735.
標(biāo)簽： Description Randomness Attribute Filtered
上傳時間： 2015-04-10
上傳用戶：ynzfm
In this paper, we describe the development of a mobile butterfly-watching learning (BWL) system to realize outdoor independent learning for mobile LEArners. The mobile butterfly-watching learning system was designed in a wireless mobile ad-hoc learning environment. This is first result to provide a cognitive tool with supporting the independent learning by applying PDA with wireless communication technology to extend learning outside of the classroom. Independent learning consists of self-selection, self-determination, self-modification, and self-checking.
標(biāo)簽： butterfly-watching development describe learning
上傳時間： 2014-11-26
上傳用戶：waizhang
蟲蟲下載站版權(quán)所有 京ICP備2021023401號-1
