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A note on learning characteristic decision trees

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LearningCharacteristicDecisionTrees

PaulDavidsson

DepartmentofComputerScience,LundUniversity

Box118,S–22100Lund,Sweden

E-mail:Paul.Davidsson@dna.lth.se

Abstract

DecisiontreesconstructedbyID3-likealgorithmssufferfromaninabilityofdetectingin-stancesofcategoriesnotpresentinthesetoftrainingexamples,i.e.,theyarediscriminativerepresentations.Instead,suchinstancesareassignedtooneoftheclassesactuallypresentinthetrainingset,resultinginundesiredmisclassifications.Twomethodsofreducingthisproblembylearningcharacteristicrepresentationsarepresented.Thecentralideabehindbothmethodsistoaugmenteachleafofthedecisiontreewithasubtreecontainingaddi-tionalinformationconcerningeachfeature’svaluesinthatleaf.Thisisdonebycomputingtwolimits(lowerandupper)foreveryfeaturefromthetraininginstancesbelongingtotheleaf.Asubtreeisthenconstructedfromtheselimitsthattestseveryfeature;ifthevalueisbelowthelowerlimitorabovetheupperlimitforsomefeature,theinstancewillberejected,i.e.,regardedasbelongingtoanovelclass.Thissubtreeisthenappendedtotheleaf.Thefirstmethodpresentedcorrespondstocreatingamaximumspecificdescription,whereasthesecondisanovelmethodthatmakesuseoftheinformationaboutthestatisticaldistributionofthefeaturevaluesthatcanbeextractedfromthetrainingexamples.Animportantprop-ertyofthenovelmethodisthatthedegreeofgeneralizationcanbecontrolled.Themeth-odsareevaluatedempiricallyintwodifferentdomains,theIrisclassificationproblemandanovelcoinclassificationproblem.Itisconcludedthatthedynamicalpropertiesofthesec-ondmethodmakesitpreferableinmostapplications.Finally,wearguethatthismethodisverygeneralinthatit,inprinciple,canbeappliedtoanyempiricallearningalgorithm.

1Introduction

Oneoftheoftenignoredproblemsforalearningsystemistoknowwhenitencountersanin-

stanceofanunknowncategory.Intheoreticalcontextsthisproblemisoftenregardedasbeingof

minorimportance,mainlybecauseitisassumedthattheproblemdomainsunderstudyareclosed

(i.e.,allrelevantinformationisknowninadvanced).However,inmanypracticalapplicationsit

cannotbeassumedthateverycategoryisrepresentedinthesetoftrainingexamples(i.e.,they

areopendomains)andsometimesthecostofamisclassificationistoohigh.Whatisneededin

suchsituationsistheabilitytorejectinstancesofcategoriesthatthesystemhasnotbeentrained

on.Forexample,considerthedecisionmechanisminacoin-sortingmachineofthekindoften

usedinbankoffices.Itstaskistosortandcountalimitednumberofdifferentcoins(forinstance,

aparticularcountry’s),andtorejectallothercoins.Supposingthatthisdecisionmechanismis

tobelearned,itisforpracticalreasonsimpossibletotrainthelearningsystemoneverypossible

kindofcoin,genuineorfaked.Rather,itisdesiredthatthesystemshouldbetrainedonlyon

thekindsofcoinsitissupposedtoaccept.Anotherexamplearedecisionsupportsystems,forfeature2

feature1

Atleast,intheoriginalversionsofthesealgorithms.2TheMethods

Bothmethodsarebasedontheideaofaugmentingeachleafofthetreeresultingfromadecision

treealgorithmwithasubtree.Thepurposeofthesesubtreesistoimposefurtherrestrictionson

thefeaturevalues.Alowerandaupperlimitarecomputedforeveryfeature.Thesewillserve

astests:ifthefeaturevalueoftheinstancetobeclassifiedisbelowthelowerlimitorabove

theupperlimitforoneormoreofthefeatures,theinstancewillberejected,i.e.,regardedas

belongingtoanovelclass,otherwiseitwillbeclassifiedaccordingtotheoriginaldecisiontree.

Thus,whenanewinstanceistobeclassified,thedecisiontreeisfirstappliedasusual,andthen,

whenaleafwouldhavebeenreached,everyfeatureoftheinstanceischeckedtoseeifitbelongs

totheintervaldefinedbythelowerandtheupperlimit.Ifallfeaturesofthenewinstanceare

insidetheirintervaltheclassificationisstillvalid,otherwisetheinstancewillberejected.

Inthefirstmethodwecomputetheminimumandmaximumfeaturevaluefromthetraining

instancesoftheleafandletthesebethelowerandupperlimitsrespectively.Thisapproachwill

yieldamaximumspecificdescription(cf.themodificationofCN2byHolteetal.[3]).

Whilebeingintuitiveandstraight-forward,thismethodisalsoratherstaticinthesensethat

thereisnowayofcontrollingthevaluesofthelimits,i.e.,thedegreeofgeneralization.Such

anabilitywouldbedesirable,forexample,whensomeinstancesthatwouldhavebeencorrectly

classifiedbytheoriginaldecisiontreearerejectedbytheaugmentedtree(whichhappensifany

ofitsfeaturevaluesisonthewrongsideofalimit).Actually,thereisatrade-offbetweenthe

numberoffailuresofthiskindandthenumberofmisclassifiedinstances.Howitshouldbe

balancedis,ofcourse,dependentoftheapplication(i.e.,thecostsofmisclassificationandre-

jection).Sinceitisimpossibleintheabovemethodtobalancethistrade-off,amoredynamic

methodinwhichitcanbecontrolledhasbeendeveloped.

Thecentralideaofthisnovelmethodistomakeuseofstatisticalinformationconcerning

thedistributionofthefeaturevaluesoftheinstancesinaleaf.Foreveryfeaturewecomputethe

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博学多才的英文表达

博学多才的英文表达

博学多才的英文表达

Erudition: A Tapestry of Intellectual Abundance.

In the hallowed halls of academia and beyond, the term

"erudition" holds a place of reverence, signifying a

profound depth and breadth of knowledge. It is a tapestry

woven with countless threads of intellectual exploration,

each contributing to a vibrant and intricate masterpiece.

Origins and Etymology.

The word "erudition" traces its roots back to the Latin

verb "erudire," meaning "to educate or train." In its

earliest usage, it referred specifically to instruction in

grammar and rhetoric. However, over time, its meaning

expanded to encompass a broader spectrum of intellectual

pursuits.

Characteristics of Erudition.

A person of erudition possesses a vast reservoir of

knowledge that spans multiple disciplines. They are well-versed in history, literature, philosophy, science, art,

新视野大学英语A第三版sin used答案+翻译

新视野大学英语A第三版sin used答案+翻译

Unit 2

1 In our class, most discussions and activities take place in

assigned small groups. These groups provide a supportive and

safe environment that promotes(促进) learning.

在我们班，大多数的讨论和活动都在指定的小组中进行。这些团体提供支持和安全的环境，促进学习。

2 It is the development strategy of the company to accelerate

its overseas expansion in order to take a slice of the world

market.

为了在世界市场上分一杯羹，加快海外扩张是公司的发展战略。

3 Nearly six million people go to see the Mona Lisa every year,

attacted by the mystery (神秘)of her you’re not looking

at her,she seems to be smiling; when you look at her, she

stops.

每年有近六百万的人去看《蒙娜丽莎》，这是她微笑的神秘之处。当你不看她的时候，她似乎在微笑;你看着她，她就停了下来。

4 To live with a family whose native language is English is the

ideal way to further improve one's English and to gain

insight(了解)intoits culture.

与母语是英语的一个家庭一起生活是最理想的方式进一步提高自己的英语和了解一起文化。 5 There is a real need to boost（提高）academic achievement in

雷达天线扫描方式的自动识别方法

雷达天线扫描方式的自动识别方法

雷达天线扫描方式的自动识别方法

李程;王伟;施龙飞;王雪松

【摘 要】In order to recognize radar antenna scan type

(AST)automatically,an improved antenna scan pattern simulator was

developed,and features extraction and automatic recognition methods for

electronic scan type (EST)and mechanical scan type (MST)were studied

respectively. EST was firstly distinguished from MST based on the

characteristic parameters extracted from the maximum main beam pulse

sequence;then 8 MSTs were automatically recognized based on the

parameters extracted from the pulse sequence in a scan period.The

simulation results show that it is able to distinguish between one-dimensional EST,two-dimensional EST and MSTs by this

method.Moreover,the correct recognition ratio of MSTs by support vector

machine decision tree (SVMDT)is higher than that by decision tree (DT).%为了对雷达天线扫描方式进行自动识别，改进开发了天线扫描方式模拟器，并分别研究了电子扫描和机械扫描的特征提取和识别方法。基于最大主瓣脉冲序列的特征参数实现电子扫描和机械扫描的区分，然后基于单个天线扫描周期脉冲序列的特征参数实现8种机械扫描方式的自动识别。仿真结果表明，本文方法能够区分一维电扫、二维电扫和机械扫描，并且采用支持向量机决策树对机械扫描方式的识别正确率高于决策树方法。

教师资格考试高级中学英语学科知识与教学能力试卷及解答参考(2024年)

教师资格考试高级中学英语学科知识与教学能力试卷及解答参考(2024年)

2024年教师资格考试高级中学英语学科知识与教学能力模拟试卷及解答参考

一、单项选择题（本大题有30小题，每小题2分，共60分）

1、The sentence “She is as tall as her brother.” is an example of a(n)_________

sentence.

A. simple

B. complex

C. compound

D. complex-complex

答案：C

解析：本题考查句子类型。复合句是由一个主句和一个或多个从句构成的句子。题干中，“She is as tall as her brother.”由主句“she is as tall as her brother”和as引导的比较状语从句组成，因此为复合句。A项意为简单句，由一个主语和一个谓语构成；B项意为复杂句，由一个主句和一个或多个从句构成；D项意为复杂复合句，由两个或两个以上的主句和从句构成。所以选C。

2、In the following sentence, the word “it” refers to_________.

The teacher wrote a story on the blackboard, and the students were all

looking at ________.

A. it B. the story

C. the blackboard

D. the teacher

答案：B

解析：本题考查代词指代。代词it在此处指代前面提到的名词a story，以避免重复。A项it不能指代a story；C项the blackboard不能指代a story；D项the teacher与a story没有关系。所以选B。

3、Which of the following is NOT a common error in English pronunciations