Machine Learning View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

2009

AUTHORS

Achim Hoffmann , Ashesh Mahidadia

ABSTRACT

The purpose of this chapter is to present fundamental ideas and techniques of machine learning suitable for the field of this book, i.e., for automated scientific discovery. The chapter focuses on those symbolic machine learning methods, which produce results that are suitable to be interpreted and understood by humans. This is particularly important in the context of automated scientific discovery as the scientific theories to be produced by machines are usually meant to be interpreted by humans. This chapter contains some of the most influential ideas and concepts in machine learning research to give the reader a basic insight into the field. After the introduction in Sect. 1, general ideas of how learning problems can be framed are given in Sect. 2. The section provides useful perspectives to better understand what learning algorithms actually do. Section 3 presents the Version space model which is an early learning algorithm as well as a conceptual framework, that provides important insight into the general mechanisms behind most learning algorithms. In section 4, a family of learning algorithms, the AQ family for learning classification rules is presented. The AQ family belongs to the early approaches in machine learning. The next, Sect. 5 presents the basic principles of decision tree learners. Decision tree learners belong to the most influential class of inductive learning algorithms today. Finally, a more recent group of learning systems are presented in Sect. 6, which learn relational concepts within the framework of logic programming. This is a particularly interesting group of learning systems since the framework allows also to incorporate background knowledge which may assist in generalisation. Section 7 discusses Association Rules – a technique that comes from the related field of Data mining. Section 8 presents the basic idea of the Naive Bayesian Classifier. While this is a very popular learning technique, the learning result is not well suited for human comprehension as it is essentially a large collection of probability values. In Sect. 9, we present a generic method for improving accuracy of a given learner by generatingmultiple classifiers using variations of the training data. While this works well in most cases, the resulting classifiers have significantly increased complexity and, hence, tend to destroy the human readability of the learning result that a single learner may produce. Section 10 contains a summary, mentions briefly other techniques not discussed in this chapter and presents outlook on the potential of machine learning in the future. More... »

PAGES

7-52

Book

TITLE

Scientific Data Mining and Knowledge Discovery

ISBN

978-3-642-02787-1
978-3-642-02788-8

Author Affiliations

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/978-3-642-02788-8_2

DOI

http://dx.doi.org/10.1007/978-3-642-02788-8_2

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1004818584


Indexing Status Check whether this publication has been indexed by Scopus and Web Of Science using the SN Indexing Status Tool
Incoming Citations Browse incoming citations for this publication using opencitations.net

JSON-LD is the canonical representation for SciGraph data.

TIP: You can open this SciGraph record using an external JSON-LD service: JSON-LD Playground Google SDTT

[
  {
    "@context": "https://springernature.github.io/scigraph/jsonld/sgcontext.json", 
    "about": [
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/0801", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Artificial Intelligence and Image Processing", 
        "type": "DefinedTerm"
      }, 
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/08", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Information and Computing Sciences", 
        "type": "DefinedTerm"
      }
    ], 
    "author": [
      {
        "affiliation": {
          "alternateName": "UNSW Australia", 
          "id": "https://www.grid.ac/institutes/grid.1005.4", 
          "name": [
            "University of New South Wales, Sydney, 2052, NSW, Australia"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Hoffmann", 
        "givenName": "Achim", 
        "id": "sg:person.014146665530.65", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.014146665530.65"
        ], 
        "type": "Person"
      }, 
      {
        "familyName": "Mahidadia", 
        "givenName": "Ashesh", 
        "id": "sg:person.01170024322.35", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01170024322.35"
        ], 
        "type": "Person"
      }
    ], 
    "datePublished": "2009", 
    "datePublishedReg": "2009-01-01", 
    "description": "The purpose of this chapter is to present fundamental ideas and techniques of machine learning suitable for the field of this book, i.e., for automated scientific discovery. The chapter focuses on those symbolic machine learning methods, which produce results that are suitable to be interpreted and understood by humans. This is particularly important in the context of automated scientific discovery as the scientific theories to be produced by machines are usually meant to be interpreted by humans. This chapter contains some of the most influential ideas and concepts in machine learning research to give the reader a basic insight into the field. After the introduction in Sect. 1, general ideas of how learning problems can be framed are given in Sect. 2. The section provides useful perspectives to better understand what learning algorithms actually do. Section 3 presents the Version space model which is an early learning algorithm as well as a conceptual framework, that provides important insight into the general mechanisms behind most learning algorithms. In section 4, a family of learning algorithms, the AQ family for learning classification rules is presented. The AQ family belongs to the early approaches in machine learning. The next, Sect. 5 presents the basic principles of decision tree learners. Decision tree learners belong to the most influential class of inductive learning algorithms today. Finally, a more recent group of learning systems are presented in Sect. 6, which learn relational concepts within the framework of logic programming. This is a particularly interesting group of learning systems since the framework allows also to incorporate background knowledge which may assist in generalisation. Section 7 discusses Association Rules \u2013 a technique that comes from the related field of Data mining. Section 8 presents the basic idea of the Naive Bayesian Classifier. While this is a very popular learning technique, the learning result is not well suited for human comprehension as it is essentially a large collection of probability values. In Sect. 9, we present a generic method for improving accuracy of a given learner by generatingmultiple classifiers using variations of the training data. While this works well in most cases, the resulting classifiers have significantly increased complexity and, hence, tend to destroy the human readability of the learning result that a single learner may produce. Section 10 contains a summary, mentions briefly other techniques not discussed in this chapter and presents outlook on the potential of machine learning in the future.", 
    "editor": [
      {
        "familyName": "Gaber", 
        "givenName": "Mohamed Medhat", 
        "type": "Person"
      }
    ], 
    "genre": "chapter", 
    "id": "sg:pub.10.1007/978-3-642-02788-8_2", 
    "inLanguage": [
      "en"
    ], 
    "isAccessibleForFree": false, 
    "isPartOf": {
      "isbn": [
        "978-3-642-02787-1", 
        "978-3-642-02788-8"
      ], 
      "name": "Scientific Data Mining and Knowledge Discovery", 
      "type": "Book"
    }, 
    "name": "Machine Learning", 
    "pagination": "7-52", 
    "productId": [
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1007/978-3-642-02788-8_2"
        ]
      }, 
      {
        "name": "readcube_id", 
        "type": "PropertyValue", 
        "value": [
          "16f5b48901ed4f2e7a68edd08df00be09094768f3a7244fbd1114dbfc30d174e"
        ]
      }, 
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1004818584"
        ]
      }
    ], 
    "publisher": {
      "location": "Berlin, Heidelberg", 
      "name": "Springer Berlin Heidelberg", 
      "type": "Organisation"
    }, 
    "sameAs": [
      "https://doi.org/10.1007/978-3-642-02788-8_2", 
      "https://app.dimensions.ai/details/publication/pub.1004818584"
    ], 
    "sdDataset": "chapters", 
    "sdDatePublished": "2019-04-15T17:55", 
    "sdLicense": "https://scigraph.springernature.com/explorer/license/", 
    "sdPublisher": {
      "name": "Springer Nature - SN SciGraph project", 
      "type": "Organization"
    }, 
    "sdSource": "s3://com-uberresearch-data-dimensions-target-20181106-alternative/cleanup/v134/2549eaecd7973599484d7c17b260dba0a4ecb94b/merge/v9/a6c9fde33151104705d4d7ff012ea9563521a3ce/jats-lookup/v90/0000000001_0000000264/records_8681_00000008.jsonl", 
    "type": "Chapter", 
    "url": "http://link.springer.com/10.1007/978-3-642-02788-8_2"
  }
]
 

Download the RDF metadata as:  json-ld nt turtle xml License info

HOW TO GET THIS DATA PROGRAMMATICALLY:

JSON-LD is a popular format for linked data which is fully compatible with JSON.

curl -H 'Accept: application/ld+json' 'https://scigraph.springernature.com/pub.10.1007/978-3-642-02788-8_2'

N-Triples is a line-based linked data format ideal for batch operations.

curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1007/978-3-642-02788-8_2'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/978-3-642-02788-8_2'

RDF/XML is a standard XML format for linked data.

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/978-3-642-02788-8_2'


 

This table displays all metadata directly associated to this object as RDF triples.

71 TRIPLES      22 PREDICATES      27 URIs      20 LITERALS      8 BLANK NODES

Subject Predicate Object
1 sg:pub.10.1007/978-3-642-02788-8_2 schema:about anzsrc-for:08
2 anzsrc-for:0801
3 schema:author N4d954283b8f14cf988338fad95d2e9a9
4 schema:datePublished 2009
5 schema:datePublishedReg 2009-01-01
6 schema:description The purpose of this chapter is to present fundamental ideas and techniques of machine learning suitable for the field of this book, i.e., for automated scientific discovery. The chapter focuses on those symbolic machine learning methods, which produce results that are suitable to be interpreted and understood by humans. This is particularly important in the context of automated scientific discovery as the scientific theories to be produced by machines are usually meant to be interpreted by humans. This chapter contains some of the most influential ideas and concepts in machine learning research to give the reader a basic insight into the field. After the introduction in Sect. 1, general ideas of how learning problems can be framed are given in Sect. 2. The section provides useful perspectives to better understand what learning algorithms actually do. Section 3 presents the Version space model which is an early learning algorithm as well as a conceptual framework, that provides important insight into the general mechanisms behind most learning algorithms. In section 4, a family of learning algorithms, the AQ family for learning classification rules is presented. The AQ family belongs to the early approaches in machine learning. The next, Sect. 5 presents the basic principles of decision tree learners. Decision tree learners belong to the most influential class of inductive learning algorithms today. Finally, a more recent group of learning systems are presented in Sect. 6, which learn relational concepts within the framework of logic programming. This is a particularly interesting group of learning systems since the framework allows also to incorporate background knowledge which may assist in generalisation. Section 7 discusses Association Rules – a technique that comes from the related field of Data mining. Section 8 presents the basic idea of the Naive Bayesian Classifier. While this is a very popular learning technique, the learning result is not well suited for human comprehension as it is essentially a large collection of probability values. In Sect. 9, we present a generic method for improving accuracy of a given learner by generatingmultiple classifiers using variations of the training data. While this works well in most cases, the resulting classifiers have significantly increased complexity and, hence, tend to destroy the human readability of the learning result that a single learner may produce. Section 10 contains a summary, mentions briefly other techniques not discussed in this chapter and presents outlook on the potential of machine learning in the future.
7 schema:editor N920e709579e741349cc54f5bc2e628fb
8 schema:genre chapter
9 schema:inLanguage en
10 schema:isAccessibleForFree false
11 schema:isPartOf N842e94d640c340c895acddc410dd3586
12 schema:name Machine Learning
13 schema:pagination 7-52
14 schema:productId N3a94448c8c684a5b9c01367a4c5b5f7a
15 Nb1d6487bed2f46f68a8325f69c218f23
16 Nf05a6f9a471540ea8af251af4bf85206
17 schema:publisher N4019f191d2f945e385a2597581df4ccf
18 schema:sameAs https://app.dimensions.ai/details/publication/pub.1004818584
19 https://doi.org/10.1007/978-3-642-02788-8_2
20 schema:sdDatePublished 2019-04-15T17:55
21 schema:sdLicense https://scigraph.springernature.com/explorer/license/
22 schema:sdPublisher N08c544d047de4a29ae6972fc118dfd9a
23 schema:url http://link.springer.com/10.1007/978-3-642-02788-8_2
24 sgo:license sg:explorer/license/
25 sgo:sdDataset chapters
26 rdf:type schema:Chapter
27 N08c544d047de4a29ae6972fc118dfd9a schema:name Springer Nature - SN SciGraph project
28 rdf:type schema:Organization
29 N3a94448c8c684a5b9c01367a4c5b5f7a schema:name dimensions_id
30 schema:value pub.1004818584
31 rdf:type schema:PropertyValue
32 N4019f191d2f945e385a2597581df4ccf schema:location Berlin, Heidelberg
33 schema:name Springer Berlin Heidelberg
34 rdf:type schema:Organisation
35 N4d954283b8f14cf988338fad95d2e9a9 rdf:first sg:person.014146665530.65
36 rdf:rest Nea73d2caaa694649a49b3c3ec10611bb
37 N7c819382db274212bd4718df29a9aa47 schema:familyName Gaber
38 schema:givenName Mohamed Medhat
39 rdf:type schema:Person
40 N842e94d640c340c895acddc410dd3586 schema:isbn 978-3-642-02787-1
41 978-3-642-02788-8
42 schema:name Scientific Data Mining and Knowledge Discovery
43 rdf:type schema:Book
44 N920e709579e741349cc54f5bc2e628fb rdf:first N7c819382db274212bd4718df29a9aa47
45 rdf:rest rdf:nil
46 Nb1d6487bed2f46f68a8325f69c218f23 schema:name readcube_id
47 schema:value 16f5b48901ed4f2e7a68edd08df00be09094768f3a7244fbd1114dbfc30d174e
48 rdf:type schema:PropertyValue
49 Nea73d2caaa694649a49b3c3ec10611bb rdf:first sg:person.01170024322.35
50 rdf:rest rdf:nil
51 Nf05a6f9a471540ea8af251af4bf85206 schema:name doi
52 schema:value 10.1007/978-3-642-02788-8_2
53 rdf:type schema:PropertyValue
54 anzsrc-for:08 schema:inDefinedTermSet anzsrc-for:
55 schema:name Information and Computing Sciences
56 rdf:type schema:DefinedTerm
57 anzsrc-for:0801 schema:inDefinedTermSet anzsrc-for:
58 schema:name Artificial Intelligence and Image Processing
59 rdf:type schema:DefinedTerm
60 sg:person.01170024322.35 schema:familyName Mahidadia
61 schema:givenName Ashesh
62 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01170024322.35
63 rdf:type schema:Person
64 sg:person.014146665530.65 schema:affiliation https://www.grid.ac/institutes/grid.1005.4
65 schema:familyName Hoffmann
66 schema:givenName Achim
67 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.014146665530.65
68 rdf:type schema:Person
69 https://www.grid.ac/institutes/grid.1005.4 schema:alternateName UNSW Australia
70 schema:name University of New South Wales, Sydney, 2052, NSW, Australia
71 rdf:type schema:Organization
 




Preview window. Press ESC to close (or click here)


...