SherbyNet - /page/wikibook/entry.mu

Micron Document

<!DOCTYPE html>

<title>Instance-based learning</title>

</head>

<span id="openzim-page-title" class="mw-page-title-main"><span class="mw-page-title-main">Instance-based learning</span></span>

</h1>

</header>

<div id="mw-content-text" class="mw-body-content mw-content-ltr" lang="en" dir="ltr"><div class="mw-content-ltr mw-parser-output" lang="en" dir="ltr"><p>In <a href="Machine_learning" title="Machine learning">machine learning</a>, <b>instance-based learning</b> (sometimes called <b>memory-based learning</b><sup id="cite_ref-1" class="reference"><a href="#cite_note-1"><span class="cite-bracket">[</span>1<span class="cite-bracket">]</span></a></sup>) is a family of learning algorithms that, instead of performing explicit generalization, compare new problem instances with instances seen in training, which have been stored in memory. Because computation is postponed until a new instance is observed, these algorithms are sometimes referred to as "lazy."<sup id="cite_ref-mitchell_2-0" class="reference"><a href="#cite_note-mitchell-2"><span class="cite-bracket">[</span>2<span class="cite-bracket">]</span></a></sup>

</p><p>It is called instance-based because it constructs hypotheses directly from the training instances themselves.<sup id="cite_ref-aima733_3-0" class="reference"><a href="#cite_note-aima733-3"><span class="cite-bracket">[</span>3<span class="cite-bracket">]</span></a></sup>

This means that the hypothesis complexity can grow with the data:<sup id="cite_ref-aima733_3-1" class="reference"><a href="#cite_note-aima733-3"><span class="cite-bracket">[</span>3<span class="cite-bracket">]</span></a></sup> in the worst case, a hypothesis is a list of <i>n</i> training items and the computational complexity of <a href="Classification_(machine_learning)" class="mw-redirect" title="Classification (machine learning)">classifying</a> a single new instance is <a href="Big_O_notation" title="Big O notation"><i>O</i></a>(<i>n</i>). One advantage that instance-based learning has over other methods of machine learning is its ability to adapt its model to previously unseen data. Instance-based learners may simply store a new instance or throw an old instance away.

</p><p>Examples of instance-based learning algorithms are the <a href="K-nearest_neighbors_algorithm" title="K-nearest neighbors algorithm"><i>k</i>-nearest neighbors algorithm</a>, <a href="Kernel_method" title="Kernel method">kernel machines</a> and <a href="Radial_basis_function_network" title="Radial basis function network">RBF networks</a>.<sup id="cite_ref-mitchell_2-1" class="reference"><a href="#cite_note-mitchell-2"><span class="cite-bracket">[</span>2<span class="cite-bracket">]</span></a></sup><sup class="reference nowrap"><span title="Page / location: ch. 8">: ch. 8 </span></sup> These store (a subset of) their training set; when predicting a value/class for a new instance, they compute distances or similarities between this instance and the training instances to make a decision.

</p><p>To battle the memory complexity of storing all training instances, as well as the risk of <a href="Overfitting" title="Overfitting">overfitting</a> to noise in the training set, <i>instance reduction</i> algorithms have been proposed.<sup id="cite_ref-4" class="reference"><a href="#cite_note-4"><span class="cite-bracket">[</span>4<span class="cite-bracket">]</span></a></sup>

</p><p><br>

</p>

<ul><li><a href="Analogical_modeling" title="Analogical modeling">Analogical modeling</a></li></ul>

<div class="mw-heading mw-heading2"><h2 id="References">References</h2></div>

/* start https://en.wikipedia.org/ */

.mw-parser-output .reflist{margin-bottom:0.5em;list-style-type:decimal}@media screen{.mw-parser-output .reflist{font-size:90%}}.mw-parser-output .reflist .references{font-size:100%;margin-bottom:0;list-style-type:inherit}.mw-parser-output .reflist-columns-2{column-width:30em}.mw-parser-output .reflist-columns-3{column-width:25em}.mw-parser-output .reflist-columns{margin-top:0.3em}.mw-parser-output .reflist-columns ol{margin-top:0}.mw-parser-output .reflist-columns li{page-break-inside:avoid;break-inside:avoid-column}.mw-parser-output .reflist-upper-alpha{list-style-type:upper-alpha}.mw-parser-output .reflist-upper-roman{list-style-type:upper-roman}.mw-parser-output .reflist-lower-alpha{list-style-type:lower-alpha}.mw-parser-output .reflist-lower-greek{list-style-type:lower-greek}.mw-parser-output .reflist-lower-roman{list-style-type:lower-roman}

/* end https://en.wikipedia.org/ */

</style><div class="reflist reflist-columns references-column-width" style="column-width: 30em;">

/* start https://en.wikipedia.org/ */

.mw-parser-output cite.citation{font-style:inherit;word-wrap:break-word}.mw-parser-output .citation q{quotes:"\"""\"""'""'"}.mw-parser-output .citation:target{background-color:rgba(0,127,255,0.133)}.mw-parser-output .id-lock-free.id-lock-free a{background:url("./mw/Lock-green.svg")right 0.1em center/9px no-repeat}.mw-parser-output .id-lock-limited.id-lock-limited a,.mw-parser-output .id-lock-registration.id-lock-registration a{background:url("./mw/Lock-gray-alt-2.svg")right 0.1em center/9px no-repeat}.mw-parser-output .id-lock-subscription.id-lock-subscription a{background:url("./mw/Lock-red-alt-2.svg")right 0.1em center/9px no-repeat}.mw-parser-output .cs1-ws-icon a{background:url("./mw/Wikisource-logo.svg")right 0.1em center/12px no-repeat}body:not(.skin-timeless):not(.skin-minerva) .mw-parser-output .id-lock-free a,body:not(.skin-timeless):not(.skin-minerva) .mw-parser-output .id-lock-limited a,body:not(.skin-timeless):not(.skin-minerva) .mw-parser-output .id-lock-registration a,body:not(.skin-timeless):not(.skin-minerva) .mw-parser-output .id-lock-subscription a,body:not(.skin-timeless):not(.skin-minerva) .mw-parser-output .cs1-ws-icon a{background-size:contain;padding:0 1em 0 0}.mw-parser-output .cs1-code{color:inherit;background:inherit;border:none;padding:inherit}.mw-parser-output .cs1-hidden-error{display:none;color:var(--color-error,#d33)}.mw-parser-output .cs1-visible-error{color:var(--color-error,#d33)}.mw-parser-output .cs1-maint{display:none;color:#085;margin-left:0.3em}.mw-parser-output .cs1-kern-left{padding-left:0.2em}.mw-parser-output .cs1-kern-right{padding-right:0.2em}.mw-parser-output .citation .mw-selflink{font-weight:inherit}@media screen{.mw-parser-output .cs1-format{font-size:95%}html.skin-theme-clientpref-night .mw-parser-output .cs1-maint{color:#18911f}}@media screen and (prefers-color-scheme:dark){html.skin-theme-clientpref-os .mw-parser-output .cs1-maint{color:#18911f}}

/* end https://en.wikipedia.org/ */

</style><cite id="CITEREFWalter_DaelemansAntal_van_den_Bosch2005" class="citation book cs1"><a href="Walter_Daelemans" title="Walter Daelemans">Walter Daelemans</a>; <a href="Antal_van_den_Bosch" title="Antal van den Bosch">Antal van den Bosch</a> (2005). <i>Memory-Based Language Processing</i>. Cambridge University Press.</cite></span>

</li>

<li id="cite_note-mitchell-2"><span class="mw-cite-backlink">^ <a href="#cite_ref-mitchell_2-0"><sup><i><b>a</b></i></sup></a> <a href="#cite_ref-mitchell_2-1"><sup><i><b>b</b></i></sup></a></span> <span class="reference-text"><cite id="CITEREFTom_Mitchell1997" class="citation book cs1">Tom Mitchell (1997). <i>Machine Learning</i>. McGraw-Hill.</cite></span>

</li>

<li id="cite_note-aima733-3"><span class="mw-cite-backlink">^ <a href="#cite_ref-aima733_3-0"><sup><i><b>a</b></i></sup></a> <a href="#cite_ref-aima733_3-1"><sup><i><b>b</b></i></sup></a></span> <span class="reference-text"><a href="Stuart_J._Russell" title="Stuart J. Russell">Stuart Russell</a> and <a href="Peter_Norvig" title="Peter Norvig">Peter Norvig</a> (2003). <i><a href="Artificial_Intelligence%3A_A_Modern_Approach" title="Artificial Intelligence: A Modern Approach">Artificial Intelligence: A Modern Approach</a></i>, second edition, p. 733. Prentice Hall. <a href="ISBN_(identifier)" class="mw-redirect" title="ISBN (identifier)">ISBN</a> <bdi>0-13-080302-2</bdi></span>

</li>

<li id="cite_note-4"><span class="mw-cite-backlink"><b><a href="#cite_ref-4">^</a></b></span> <span class="reference-text"><cite id="CITEREFD._Randall_WilsonTony_R._Martinez2000" class="citation journal cs1">D. Randall Wilson; Tony R. Martinez (2000). "Reduction techniques for instance-based learning algorithms". <i><a href="Machine_Learning_(journal)" title="Machine Learning (journal)">Machine Learning</a></i>.</cite></span>

</li>

</ol></div>

<p><br>

</p>

/* start https://en.wikipedia.org/ */

.mw-parser-output .asbox{position:relative;overflow:hidden}.mw-parser-output .asbox table{background:transparent}.mw-parser-output .asbox p{margin:0}.mw-parser-output .asbox p+p{margin-top:0.25em}.mw-parser-output .asbox-body{font-style:italic}.mw-parser-output .asbox-note{font-size:smaller}.mw-parser-output .asbox .navbar{position:absolute;top:-0.75em;right:1em;display:none}.mw-parser-output :not(p):not(.asbox)+style+.asbox,.mw-parser-output :not(p):not(.asbox)+link+.asbox{margin-top:3em}

/* end https://en.wikipedia.org/ */

</style>

</div><div><div class="zim-footer">

This article is issued from <a class="external text" title="Last edited on 2025-06-25" href="https://en.wikipedia.org/wiki/?title=Instance-based_learning&oldid=1297356299">Wikipedia</a>. The text is available under <a class="external text" href="https://creativecommons.org/licenses/by-sa/4.0/deed.en">Creative Commons Attribution-Share Alike 4.0</a> unless otherwise noted. Additional terms may apply for the media files.

</div>

</div></div>

</div>

</main>

</div>

</body></html>