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\u5c3d\u7ba1\u884c\u4e1a\u4e2d\u7684\u5a92\u4f53\u5206\u6790\u548c\u6587\u5316\u5206\u6790\u7814\u7a76\u90fd\u4f7f\u7528\u4e86\u6570\u5341\u79cd\u7b97\u6cd5\uff0c\u4f46\u5728\u5b83\u4eec\u80cc\u540e\u5374\u53ea\u6709\u5c11\u91cf\u7684\u57fa\u672c\u8303\u5f0f\u3002\u6211\u4eec\u53ef\u4ee5\u5c06\u5b83\u4eec\u89c6\u4e3a\u6570\u636e\/\u4eba\u5de5\u667a\u80fd\u793e\u4f1a\u7684\u8ba4\u77e5\u7c7b\u578b\u3002\u6700\u5e38\u89c1\u7684\u4e09\u79cd\u662f\uff1a\u6570\u636e\u53ef\u89c6\u5316\u3001\u65e0\u76d1\u7763\u673a\u5668\u5b66\u4e60\u548c\u6709\u76d1\u7763\u673a\u5668\u5b66\u4e60\u3002\u5176\u4ed6\u529f\u80fd\u5305\u62ec\u7279\u5f81\u63d0\u53d6\u3001\u805a\u7c7b\u3001\u964d\u7ef4\u3001\u5206\u7c7b\u3001\u56de\u5f52\u3001\u7f51\u7edc\u79d1\u5b66\u3001\u65f6\u95f4\u5e8f\u5217\u5206\u6790\u548c\u4fe1\u606f\u68c0\u7d22\u3002\uff08\u5176\u4ed6\u4eba\u53ef\u80fd\u6709\u4e0d\u540c\u7684\u5217\u8868\uff0c\u5177\u4f53\u53d6\u51b3\u4e8e\u5404\u81ea\u9886\u57df\u3002\uff09<\/p>\n\n\n\n

\u4e8c\u3001\u6570\u5b57\u793e\u4f1a\u601d\u60f3\u7684\u53d1\u5c55\uff1a\u65f6\u95f4\u7ebf<\/strong><\/p>\n\n\n\n

\u8003\u8651\u5230\u201c\u4eba\u5de5\u667a\u80fd\u201d\u201c\u673a\u5668\u5b66\u4e60\u201d\u548c\u201c\u6570\u636e\u79d1\u5b66\u201d\u7b49\u672f\u8bed\u57282010\u5e74\u4ee3\u8fdb\u5165\u516c\u4f17\u8ba8\u8bba\u8303\u7574\u7684\u901f\u5ea6\u4e4b\u5feb\u548c\u8303\u56f4\u4e4b\u5e7f\uff0c\u8bb8\u591a\u4eba\u53ef\u80fd\u8ba4\u4e3a\u8fd9\u4e9b\u9886\u57df\u662f\u975e\u5e38\u65b0\u7684\u3002\u4e8b\u5b9e\u4e0a\uff0c\u4eca\u5929\u7528\u4e8e\u6570\u636e\u5206\u6790\u7684\u51e0\u4e4e\u6240\u6709\u60f3\u6cd5\u548c\u65b9\u6cd5\u90fd\u662f\u572819\u4e16\u7eaa\u548c20\u4e16\u7eaa\u53d1\u5c55\u8d77\u6765\u7684\u3002\u4e3a\u4e86\u8bf4\u660e\u8fd9\u4e00\u70b9\uff0c\u6211\u4ee5\u7edf\u8ba1\u5b66\u548c\u8ba1\u7b97\u6570\u636e\u5206\u6790\u7684\u65b9\u6cd5\uff0c\u6784\u5efa\u4e86\u76f8\u5173\u6982\u5ff5\u548c\u65b9\u6cd5\u7684\u65f6\u95f4\u7ebf\u3002\uff08\u5220\u589e\u5185\u5bb9\u662f\u57fa\u4e8e\u6211\u81ea\u5df1\u5728\u6570\u636e\u79d1\u5b66\u65b9\u9762\u7684\u7ecf\u9a8c\uff0c\u6709\u610f\u8bc6\u5730\u504f\u5411\u4e8e\u6587\u5316\u5206\u6790\u7814\u7a76\uff0c\u800c\u4e0d\u662f\u6240\u6709\u884c\u4e1a\u7684\u5e94\u7528\u3002\uff09<\/p>\n\n\n\n

\uff08\u4e00\uff0919\u4e16\u7eaa<\/strong><\/p>\n\n\n\n

\u5728\u5927\u4f17\u7684\u8eab\u4f53\u7279\u5f81\u4e2d\u89c2\u5bdf\u5230\u7684\u6b63\u6001\u5206\u5e03\uff1a1830\u5e74\u4ee3\u521d\u671f\u7684Adolphe Quetelet\u3002<\/p>\n\n\n\n

\u793e\u4f1a\u7269\u7406\u5b66\uff1a1835\u5e74\uff0cQuetelet\u7684\u8457\u4f5c\u300a\u793e\u4f1a\u7269\u7406\u5b66\u300b\uff08Sur L’homme et le D\u00e9veloppement de ses Facult\u00e9s\uff0c\u82f1\u8bed\u8bd1\u4e3a\u300a\u4eba\u7c7b\u8bba\u300b\uff09\u6982\u8ff0\u4e86\u201c\u793e\u4f1a\u7269\u7406\u5b66\u201d\u7684\u6982\u5ff5\u3002Quetelet\u4ece\u65e9\u5148\u4f7f\u7528\u8fc7\u5b83\u7684Compte\u90a3\u91cc\u632a\u7528\u4e86\u8fd9\u4e2a\u672f\u8bed\u3002<\/p>\n\n\n\n

\u793e\u4f1a\u5b66\uff1a1838\u5e74\uff0c\u5f97\u77e5Quetelet\u4f7f\u7528\u4e86\u4ed6\u7684\u6982\u5ff5\uff0cCompte\u5f15\u5165\u4e86\u65b0\u7684\u6982\u5ff5\u201c\u793e\u4f1a\u5b66\u201d\uff08sociologie\uff09\u3002<\/p>\n\n\n\n

\u6807\u51c6\u5dee\uff1aFrancis Galton\uff0c1860\u5e74\u672b\u3002<\/p>\n\n\n\n

\u76f8\u5173\u6027\uff1a1846\u5e74\u7684Aguste Bravais\uff1bGalton\u57281888\u5e74\u91cd\u65b0\u53d1\u73b0\u3002<\/p>\n\n\n\n

\u56de\u5f52\u5206\u6790\uff1a1880\u5e74\u4ee3\u7684Galton\u3002<\/p>\n\n\n\n

\u76f4\u65b9\u56fe\uff1a1895\u5e74\u7684Pearson\u3002<\/p>\n\n\n\n

\u56de\u5f52\u5206\u6790\uff1a\u7ecf\u8fc7\u6269\u5c55\u548c\u5f62\u5f0f\u5316\u5904\u7406\u2014\u2014Udny Yule\u548cKarl Pearson\uff0c1897\u20141903\u5e74\u3002<\/p>\n\n\n\n

\u7edf\u8ba1\u5047\u8bbe\u68c0\u9a8c\u7406\u8bba\u57fa\u7840\uff1aPearson\uff0c1900\u5e74\u3002<\/p>\n\n\n\n

\u5e42\u6b21\u6cd5\u5219\uff1a1896\u5e74\uff0c\u7ecf\u6d4e\u5b66\u5bb6Vilfredo Pareto\u53d1\u8868\u4e86\u4ed6\u5bf9\u201c80-20\u6cd5\u5219\u201d\u7684\u89c2\u5bdf\uff0c\u540e\u6765\u88ab\u79f0\u4e3a\u201c\u5e15\u7d2f\u6258\u6cd5\u5219\u201d\uff1b\u4ed6\u4e3e\u4f8b\u8bf4\u660e\u4e86\u66f4\u4e00\u822c\u7684\u201c\u5e42\u6b21\u6cd5\u5219\u201d\uff0c\u63cf\u8ff0\u4e86\u5f53\u4eca\u7f51\u7edc\u6587\u5316\u548c\u793e\u4ea4\u5a92\u4f53\u4e2d\u7684\u8bb8\u591a\u73b0\u8c61\uff0c\u4f8b\u5982\u201c\u957f\u5c3e\u7406\u8bba\u201d\u3002<\/p>\n\n\n\n

\uff08\u4e8c\uff0920\u4e16\u7eaa<\/strong><\/p>\n\n\n\n

\u4e3b\u6210\u5206\u5206\u6790\uff1a1901\u5e74\u7684Pearson\u3002<\/p>\n\n\n\n

\u56e0\u7d20\u5206\u6790\uff1a1904\u5e74\uff0cCharles Spearman\u3002<\/p>\n\n\n\n

\u591a\u53d8\u91cf\u56de\u5f52\uff1a\u7b2c\u4e00\u6b21\u4f7f\u7528\u662f\u57281900\u5e74\u4ee3\u3002<\/p>\n\n\n\n

\u9a6c\u5c14\u53ef\u592b\u94fe\uff1a1906\u5e74\uff0cAndrew Markov\u5f00\u59cb\u81f4\u529b\u4e8e\u65f6\u95f4\u5e8f\u5217\u7edf\u8ba1\u5206\u6790\u7684\u7406\u8bba\u548c\u65b9\u6cd5\uff0c\u540e\u6765\u79f0\u201c\u9a6c\u5c14\u53ef\u592b\u94fe\u201d\u3002<\/p>\n\n\n\n

\u7f51\u7edc\u5206\u6790\uff1a1930\u5e74\u4ee3\uff0cJacob Moreno\u5f00\u53d1\u4e86\u8868\u793a\u793e\u4ea4\u7f51\u7edc\u7684\u65b9\u6cd5\uff1b1950\u5e74\u4ee3\uff0c\u7528\u4e8e\u7f51\u7edc\u5206\u6790\u7684\u6d4b\u91cf\u65b9\u6cd5\u548c\u6982\u5ff5\u88ab\u5f62\u5f0f\u5316\u3002<\/p>\n\n\n\n

\u795e\u7ecf\u7f51\u7edc\uff1a1957\u5e74\uff0cFrank Rosenblatt\u5f00\u53d1\u4e86\u7528\u4e8e\u5206\u7c7b\u7684\u795e\u7ecf\u7f51\u7edc\uff08\u201c\u611f\u77e5\u5668\u201d\uff09\u3002\u8fd9\u9879\u7814\u7a76\u57fa\u4e8e\u4e4c\u514b\u5170\u88d4\u7f8e\u56fd\u79d1\u5b66\u5bb6Nicolas Rashevsky\u5f00\u53d1\u7684\u6700\u65e9\u7684\u795e\u7ecf\u7f51\u7edc\u6a21\u578b\u3002\u4ed6\u7684\u5b66\u751fWarren McCulloch\u548cWalter Pitts\u8fdb\u4e00\u6b65\u53d1\u5c55\u4e86\u4ed6\u7684\u6a21\u578b\uff0c\u5e76\u4e8e1943\u5e74\u5728Rashevsky\u7684\u6742\u5fd7\u4e0a\u53d1\u8868\u4e86\u8457\u540d\u6587\u7ae0\u3002[8]<\/p>\n\n\n\n

\u201c\u8f6f\u4ef6\u201d\uff1a1958\u5e74\uff0cJohn Tukey\u662f\u7b2c\u4e00\u4e2a\u4f7f\u7528\u6b64\u672f\u8bed\u7684\u4eba\u3002[9]<\/p>\n\n\n\n

\u591a\u7ef4\u5c3a\u5ea6\u5206\u6790\uff08MDS\uff09\uff1a1950\u5e74\u4ee3\u3002[10]<\/p>\n\n\n\n

\u652f\u6301\u5411\u91cf\u673a\uff08SVM\uff09\uff1a1963\u5e74\uff0cVladimir Vapnik\u548cAlexey Chervonenkis\uff08\u82cf\u8054\uff09\u5f00\u53d1\u4e86\u6700\u521d\u7684\u7b97\u6cd5\u3002Vapnik\u57281995\u5e74\u53d1\u5e03\u7684\u4e00\u79cd\u7b97\u6cd5\u7248\u672c\u6210\u4e3a\u6700\u53d7\u6b22\u8fce\u7684\u5206\u7c7b\u7b97\u6cd5\u4e4b\u4e00\u3002<\/p>\n\n\n\n

\u6df1\u5ea6\u5b66\u4e60\uff1a1965\u5e74\uff0cAlexey Ivakhnenko\u548cV. G. Lapa\u53d1\u5e03\u4e86\u7b2c\u4e00\u4e2a\u7528\u4e8e\u6df1\u5ea6\u5b66\u4e60\u7684\u5de5\u4f5c\u7b97\u6cd5\u3002\u57282000\u5e74\u4ee3\u540e\u671f\uff0c\u6df1\u5ea6\u5b66\u4e60\u6210\u4e3a\u6700\u6d41\u884c\u7684\u5206\u7c7b\u65b9\u6cd5\u3002Ivakhnenko\u901a\u5e38\u88ab\u79f0\u4e3a\u201c\u6df1\u5ea6\u5b66\u4e60\u4e4b\u7236\u201d\u3002<\/p>\n\n\n\n

\u5411\u91cf\u7a7a\u95f4\u6982\u5ff5\uff1a1960\u5e74\u4ee3\u672b\u671f\uff0c\u7531Gary Salton\u4e3a\u6587\u672c\u6bd4\u8f83\u800c\u5f00\u53d1\u3002<\/p>\n\n\n\n

\u63a2\u7d22\u6027\u6570\u636e\u5206\u6790\uff1a1971\u5e74\uff0cTukey\u5f00\u59cb\u53d1\u5c55\u63a2\u7d22\u6027\u6570\u636e\u5206\u6790\u7684\u601d\u60f3\u3002<\/p>\n\n\n\n

\u7edf\u8ba1\u7f16\u7a0b\u8bed\u8a00\uff1a1976\u5e74\uff0cJohn Chambers\u7b49\u4eba\u5728\u8d1d\u5c14\u5b9e\u9a8c\u5ba4\u5f00\u53d1\u4e86\u540d\u4e3a\u201cS\u201d\u7684\u8bed\u8a00\uff0c\u5b83\u4f7f\u63a2\u7d22\u6027\u6570\u636e\u5206\u6790\u6210\u4e3a\u53ef\u80fd\uff0c\u56e0\u4e3a\u5b83\u4f7f\u7814\u7a76\u4eba\u5458\u53ef\u4ee5\u4ece\u547d\u4ee4\u884c\u6267\u884c\u5404\u79cd\u7edf\u8ba1\u548c\u6570\u636e\u5206\u6790\uff0c\u5e76\u5236\u4f5c\u56fe\u8868\u3002[11]\u201cR\u201d\u8bed\u8a00\u540e\u6765\u4ece\u201cS\u201d\u8bed\u8a00\u53d1\u5c55\u800c\u6765\u3002<\/p>\n\n\n\n

\u53cd\u5411\u4f20\u64ad\uff1a1986\u5e74\uff0c\u8fd9\u79cd\u65b0\u7684\u6df1\u5ea6\u7f51\u7edc\u6559\u5b66\u65b9\u6cd5\u5728David Rumelhart\uff0cGeoffrey Hinton\u548cRonald Williams\u4e8e\u300a\u81ea\u7136\u300b\u6742\u5fd7\u4e0a\u53d1\u8868\u540e\u5f00\u59cb\u6d41\u884c\u3002[12]<\/p>\n\n\n\n

\u7f51\u9875\u7b49\u7ea7\uff08PageRank\uff09\uff1a1996\u5e74\uff0cLarry Page\u548cSergey Brin\u5f00\u53d1\u4e86\u7f51\u9875\u7b49\u7ea7\u7b97\u6cd5\u3002\u8fd9\u4e2a\u60f3\u6cd5\u63d0\u51fa\u5f97\u5f88\u65e9\uff0c\u53ef\u4ee5\u8ffd\u6eaf\u52301950\u5e74\u4ee3\u6587\u732e\u8ba1\u91cf\u5b66\u548c\u79d1\u5b66\u8ba1\u91cf\u5b66\u7684\u521b\u59cb\u4ebaEugene Garfield\u7684\u5de5\u4f5c\uff08\u5c3d\u7ba1\u7f51\u9875\u7b49\u7ea7\u5e76\u4e0d\u662f\u6570\u636e\u5206\u6790\u4e2d\u7684\u5173\u952e\u7b97\u6cd5\uff0c\u4f46\u5b83\u5bf9\u4eba\u4eec\u4e0e\u4fe1\u606f\u548c\u6587\u5316\u5185\u5bb9\u5728\u7ebf\u4e92\u52a8\u7684\u65b9\u5f0f\u4ea7\u751f\u4e86\u6839\u672c\u5f71\u54cd\uff09\u3002<\/p>\n\n\n\n

\u7269\u2014\u7269\u534f\u540c\u8fc7\u6ee4\uff08Item-item collaborative filtering\uff09\uff1a1998\u5e74\uff0c\u4e9a\u9a6c\u900a\u53d1\u660e\u5e76\u5f00\u59cb\u4f7f\u7528\u7269\u2014\u7269\u534f\u540c\u8fc7\u6ee4\uff0c\u8fd9\u662f\u4e00\u79cd\u63a8\u8350\u7b97\u6cd5\uff0c\u53ef\u4ee5\u6839\u636e\u4eba\u4eec\u5bf9\u8fd9\u4e9b\u5bf9\u8c61\u7684\u8bc4\u4ef7\u6765\u8ba1\u7b97\u5bf9\u8c61\uff08\u4f8b\u5982\u4e66\u7c4d\uff09\u4e4b\u95f4\u7684\u76f8\u4f3c\u5ea6\u3002[13]<\/p>\n\n\n\n

\uff08\u4e09\uff0921\u4e16\u7eaa<\/strong><\/p>\n\n\n\n

\u9690\u542b\u72c4\u5229\u514b\u96f7\u5206\u5e03\uff08LDA\uff09\uff1a2003\u5e74\uff0cDavid Blei\u3001Andrew Ng\u548cMichael I. Jordan\u53d1\u5e03\u4e86\u6587\u672c\u6587\u4ef6\u4e2d\u7684\u201c\u4e3b\u9898\u5efa\u6a21\u201d\u6a21\u578b\uff0c\u79f0\u4e3a\u9690\u542b\u72c4\u5229\u514b\u96f7\u5206\u5e03\u3002J. K. Pritchard\u3001M. Stephens\u548cP. Donnelly\u4e8e1999\u5e74\u53d1\u8868\u4e86\u6700\u521d\u7684\u4e3b\u9898\u5efa\u6a21\u8bba\u6587\u3002<\/p>\n\n\n\n

\u6df1\u5ea6\u5b66\u4e60\u8d77\u6b65\uff1a\u5f53\u524d\u7684\u6df1\u5ea6\u5b66\u4e60\u7814\u7a76\u6d6a\u6f6e\u662f\u7531Geoffrey Hinton\u3001Simon Osindero\u548cYee-Whye Teh\u4e8e2006\u5e74\u51fa\u7248\u7684\u300a\u57fa\u4e8e\u6df1\u5ea6\u7f6e\u4fe1\u7f51\u7edc\u7684\u5feb\u901f\u5b66\u4e60\u7b97\u6cd5\u300b\u5f00\u59cb\u3002[14]\u7ed3\u5408\u4f7f\u7528\u53ef\u7528\u4e8e\u8bad\u7ec3\u7684\u66f4\u5927\u6570\u636e\uff0c\u4f7f\u7528\u8ba1\u7b97\u673a\u96c6\u7fa4\u548cGPU\uff0c\u5728\u7ebf\u5171\u4eab\u6570\u636e\u96c6\u548c\u4ee3\u7801\u4ee5\u53ca\u8bf8\u5982ImageNet\u4e4b\u7c7b\u7684\u7ade\u8d5b\uff0c\u5e26\u6765\u4e86\u975e\u5e38\u5feb\u7684\u8fdb\u6b65\u3002<\/p>\n\n\n\n

\u5c06\u6df1\u5ea6\u5b66\u4e60\u7528\u4e8e\u56fe\u50cf\u5206\u7c7b\uff1a2012\u5e74\uff0cAlex Krizhevsky\u3001Ilya Sutskever\u548cGeoffrey Hinton\u64b0\u5199\u7684\u8bba\u6587\u300a\u5177\u6709\u6df1\u5ea6\u5377\u79ef\u795e\u7ecf\u7f51\u7edc\u7684ImageNet\u5206\u7c7b\u300b\u663e\u793a\u51fa\u201c\u6df1\u5ea6\u5b66\u4e60\u4f18\u4e8e\u5176\u4ed6\u56fe\u50cf\u5206\u7c7b\u65b9\u6cd5\u201d\u3002[15]<\/p>\n\n\n\n

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\u4f46\u662f\u8fd9\u4e9b\u201c\u63a2\u7d22\u201d\u7684\u76ee\u6807\u662f\u4ec0\u4e48\uff1f\u6211\u4eec\u5728\u6d4b\u91cf\u548c\u6bd4\u8f83\u4ec0\u4e48\uff1f\u4f5c\u4e3a\u7ed3\u8bba\uff0c\u6211\u60f3\u5bf9\u8fd9\u4e2a\u95ee\u9898\u505a\u51fa\u4e00\u4e2a\u56de\u7b54\u3002\u89c2\u5bdf\u548c\u5206\u6790\u6587\u5316\u610f\u5473\u7740\u80fd\u591f\u7ed8\u5236\u548c\u6d4b\u91cf\u4e09\u4e2a\u57fa\u672c\u7279\u5f81\uff0c\u5373\u591a\u6837\u6027\u3001\u7ed3\u6784\u6027\uff08\u4f8b\u5982\u96c6\u7fa4\u7f51\u7edc\u548c\u5176\u4ed6\u7c7b\u578b\u7684\u5173\u7cfb\uff09\u548c\u52a8\u6001\u6027\uff08\u65f6\u95f4\u53d8\u5316\uff09\u3002\u5728\u6587\u5316\u73af\u5883\u4e2d\uff0c\u6211\u4eec\u53ef\u80fd\u671f\u671b\u8bb8\u591a\u4f5c\u54c1\u9075\u5faa\u67d0\u4e9b\u89c4\u8303\u6027\u7f8e\u5b66\u6216\u4f7f\u7528\u6a21\u677f\uff08\u4f8b\u5982\uff0c\u5e94\u7528\u7a0b\u5e8f\u63d0\u4f9b\u7684Instagram\u8fc7\u6ee4\u5668\uff0c\u6216\u6210\u5343\u4e0a\u4e07\u7684\u5efa\u8bae\u4e2d\u63cf\u8ff0\u548c\u8bf4\u660e\u7684\u4e3b\u9898\uff09\uff0c\u4f46\u6211\u4eec\u8fd8\u53ef\u4ee5\u770b\u5230\u7b2c\u56db\u90e8\u5206\u7279\u70b9\uff1a\u53d8\u5316\u6027\u3002\u4f8b\u5982\uff0c\u6211\u4eec\u5982\u679c\u5206\u6790Instagram\u56fe\u7247\u7684\u6837\u672c\uff0c\u53ef\u4ee5\u9996\u5148\u68c0\u6d4b\u51fa\u73b0\u5728\u8bb8\u591a\u5e16\u5b50\u4e2d\u7684\u4e3b\u9898\uff0c\u7136\u540e\u67e5\u770b\u4e0e\u8fd9\u4e9b\u4e3b\u9898\u7684\u504f\u5dee\u4ee5\u53ca\u4e0d\u9075\u5faa\u4efb\u4f55\u4e3b\u9898\u7684\u56fe\u50cf\u3002\u4f46\u662f\u6211\u4eec\u4e0d\u60f3\u5047\u8bbe\u4e0e\u7c7b\u578b\uff08\u6216\u5e73\u5747\u503c\uff0c\u6216\u53ef\u4ee5\u8ba1\u7b97\u6570\u636e\u96c6\u7684\u5176\u4ed6\u7edf\u8ba1\u6570\u636e\uff09\u4e4b\u95f4\u7684\u504f\u5dee\u662f\u6240\u6709\u6587\u5316\u60c5\u51b5\u7684\u5fc5\u8981\u5ea6\u91cf\u3002\u9488\u5bf9\u4e0d\u540c\u7c7b\u578b\u7684\u5a92\u4f53\u548c\u6587\u5316\u9886\u57df\uff0c\u5236\u5b9a\u6709\u5173\u6587\u5316\u591a\u6837\u6027\u3001\u7ed3\u6784\u6027\u3001\u52a8\u6001\u6027\u548c\u53d8\u5316\u6027\u7684\u5408\u9002\u65b9\u6848\uff0c\u672c\u8eab\u5c31\u662f\u4e00\u9879\u91cd\u5927\u7684\u7406\u8bba\u548c\u5b9e\u8df5\u4efb\u52a1\u3002\u6211\u8ba4\u4e3a\u8fd9\u662f\u672a\u6765\u51e0\u5e74\u6587\u5316\u5206\u6790\u7684\u4e2d\u5fc3\u4efb\u52a1\u3002<\/p>\n\n\n\n

Can We Think Without Categories?<\/strong><\/p>\n\n\n\n

Lev Manovich<\/p>\n\n\n\n

Abstract: <\/strong>In this article methods developed for the purpose of what I call \u201cMedia Analytics\u201d are contextualized, put into a historical framework and discussed in regard to their relevance for \u201cCultural Analytics\u201d. Large-scale analysis of media and interactions enable NGOs, small and big businesses, scientific research and civic media to create insight and information on various cultural phenomena. They provide quantitative analytical data about aspects of digital culture and are instrumental in designing procedural components for digital applications such as search, recommendations, and contextual advertising.<\/p>\n\n\n\n

A survey on key texts and propositions from 1830 on until the present sketches the development of \u201cData Society’s Mind\u201d. I propose that even though Cultural Analytics research uses dozens of algorithms, behind them there is a small number of fundamental paradigms. We can think them as types of data society’s and AI society’s cognition. The three most general paradigmatic approaches are data visualization, unsupervised machine learning, and supervised machine learning.<\/p>\n\n\n\n

 I will discuss important challenges for Cultural Analytics research. Now that we have very large cultural data available, and our computers can do complex analysis quite quickly, how shall we look at culture? Do we only use computational methods to provide better answers to questions already established in the 19th and 20th century humanities paradigms, or do these methods allow fundamentally different new concepts?<\/p>\n\n\n\n

Keywords:<\/strong> Cultural Analysis; Media Analysis; Humanities Paradigm<\/p>\n\n\n\n

\uff08\u7f16\u8f91\uff1a\u59dc\u6587\u6d9b\uff09<\/p>\n\n\n\n

\u6ce8\u91ca\uff1a<\/strong><\/p>\n\n\n\n

\u672c\u6587\u6e90\u81eaMathias Fuchs, Ram\u00f3n Reichert, \u201cRethinking AI. Neural Networks, Biometrics and the New Artificial Intelligence,\u201dDigital Culture & Society<\/em> (DCS<\/em>), Special issue, vol. 4, no. 1, Bielefeld: TranscriptVerlag, 2018, pp. 17-28.<\/p>\n\n\n\n

[1]Lev Manovich, \u201c100 Billion Data Rows per Second: Media Analytics in the Early 21st Century,\u201dInternational Journal of Communication<\/em>, no. 12, 2018, pp. 473-488.<\/p>\n\n\n\n

[2]Alexis C. Mardrigal, \u201cHow Netflix Reverse Engineered Hollywood,\u201dThe Atlantic<\/em>, January 2, 2014, https:\/\/www.theatlantic.com\/technology\/archive\/2014\/01\/how-netflix-reverse-engineered-hollywood\/282679\/.<\/p>\n\n\n\n

[3]See Lev Manovich, \u201cCultural Analytics: Visualizing Cultural Patterns in the Era of ‘More Media’,\u201dDomus<\/em>, March 2009.<\/p>\n\n\n\n

[4]Saleh Babak et al., \u201cToward Automated Discovery of Artistic Influence,\u201d https:\/\/arxiv.org\/abs\/1408.3218, 2014.<\/p>\n\n\n\n

[5]Joan Serr\u00e0 et al., \u201cMeasuring the Evolution of Contemporary Western Popular Music,\u201d https:\/\/www.nature.com\/articles\/srep00521, 2012.<\/p>\n\n\n\n

[6]James E. Cutting, Ayse Candan, \u201cShot Durations, Shot Classes, and the Increased Pace of Popular Movies,\u201dProjections<\/em>, in The Journal for Movies and Mind<\/em>, vol. 2, no. 9, 2015, pp. 40-62, http:\/\/people.psych.cornell.edu\/~jec7\/pubs\/cutting&candanProj15.pdf.<\/p>\n\n\n\n

[7]See Geoffrey Winthrop-Young, Ilinca Irascu, Jussi Parikka, \u201cCultural Techniques (Special Issue),\u201dTheory, Culture & Society<\/em>, vol. 30, no. 6, November 2013.<\/p>\n\n\n\n

[8]Warren McCulloch, Walter Pitts, \u201cA Logical Calculus of the Ideas Immanent in Nervous Activity,\u201dThe Bulletin of Mathematical Biophysics<\/em>, vol. 5, no. 4, 1943, pp. 115-133, doi: 10.1007\/BF02478259.<\/p>\n\n\n\n

[9]John Tukey, \u201cThe Teaching of Concrete Mathematics,\u201dThe American Mathematical Monthly<\/em>, vol. 65, no. 1, 1958, pp. 1-9.<\/p>\n\n\n\n

[10]Warren S. Torgerson, Theory and Methods of Scaling<\/em>, New York: Wiley; Paul E. Green, \u201cMarketing Applications of MDS: Assessment and Outlook,\u201dJournal of Marketing<\/em>, vol. 39, no. 1, 1975, pp. 24-31.<\/p>\n\n\n\n

[11]John Chambers, \u201cStages in the Evolution of S,\u201d March 7, 2000, http:\/\/ect.bell-labs.com\/sl\/S\/history. html, accessed August 29, 2016.<\/p>\n\n\n\n

[12]David Rumelhart, Geoffrey Hinton, Ronald Williams, \u201cLearning Representations by Back-Propagating Errors,\u201dNature<\/em>, vol. 323, no. 6088, 1968, p. 533.<\/p>\n\n\n\n

[13]Badrul Sarwar et al, \u201cItem-Based Collaborative Filtering Recommendation Algorithms,\u201d Proceedings of the 10th International Conference on World Wide Web (ACM), 2001, pp. 285-295.<\/p>\n\n\n\n

[14]Geoffrey Hinton, Simon Osindero, Yee-Whye Teh, \u201cA Fast Learning Algorithm for Deep Belief Nets,\u201dNeural Computation<\/em>, vol. 18, no.7, 2006, pp. 1527-1554.<\/p>\n\n\n\n

[15]Alex Krizhevsky, Ilya Sutskever, Geoffrey Hinton,\u201cImageNet Classification with Deep Convolutional Neural Networks,\u201dAdvanced in Neural Information Processing Systems<\/em>, vol. 25, 2012, http:\/\/papers.nips. cc\/paper\/4824-imagenet-classification-with-deep-convolutional-neural-networks, accessed August 8, 2016.<\/p>\n\n\n\n

[16]Nicolas Truong, Nicolas Weill, \u201cA Decade after His Death, French Sociologist Pierre Bourdieu Stands Tall,\u201dThe Guardian<\/em>, February 21, 2012, https:\/\/www.theguardian.com\/world\/2012\/feb\/21\/pierre-bourdieuphilosophy-most-quoted.<\/p>\n\n\n\n

[17]Jordan DeLong, \u201cHorseshoes, Handgrenades, and Model Fitting: The Lognormal Distribution is a Pretty Good Model for Shot-Length Distribution of Hollywood Films,\u201dDigital Scholarship in the Humanities<\/em>, vol. 30, no. 1, 2015, pp. 129-136.<\/p>","protected":false},"excerpt":{"rendered":"

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