{"title":"Introduction to R","authors":"J. Zizka, F. Dařena, Arnošt Svoboda","doi":"10.1201/9780429469275-2","DOIUrl":"https://doi.org/10.1201/9780429469275-2","url":null,"abstract":"","PeriodicalId":258194,"journal":{"name":"Text Mining with Machine Learning","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127629607","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Let’s now look at the AdaBoost setup in more detail. • Loss ` = exp (exponential loss). It is also common to use the logistic loss ln(1 + exp(·)), but for simplicity we’ll use the standard choice. • Examples ((xi, yi))i=1 with xi ∈ X and yi ∈ {−1,+1}. The main thing to note is that X is just some opaque set, we are not assuming vector space structure, and can not form inner products 〈w, x〉. • Elementary hypotheses H = (hj)j=1, where hj : X → [−1,+1] for each j. Rather than interacting with examples in X directly, boosting algorithms embed them in a vector space via these functions H. For example, a vector v ∈ R is now interpreted as a linear combination of elements of H, and predictions on a new example x ∈ X are computed as x 7→ ∑
{"title":"Adaboost","authors":"Jan Žižka, F. Dařena, Arnošt Svoboda","doi":"10.1201/9780429469275-9","DOIUrl":"https://doi.org/10.1201/9780429469275-9","url":null,"abstract":"Let’s now look at the AdaBoost setup in more detail. • Loss ` = exp (exponential loss). It is also common to use the logistic loss ln(1 + exp(·)), but for simplicity we’ll use the standard choice. • Examples ((xi, yi))i=1 with xi ∈ X and yi ∈ {−1,+1}. The main thing to note is that X is just some opaque set, we are not assuming vector space structure, and can not form inner products 〈w, x〉. • Elementary hypotheses H = (hj)j=1, where hj : X → [−1,+1] for each j. Rather than interacting with examples in X directly, boosting algorithms embed them in a vector space via these functions H. For example, a vector v ∈ R is now interpreted as a linear combination of elements of H, and predictions on a new example x ∈ X are computed as x 7→ ∑","PeriodicalId":258194,"journal":{"name":"Text Mining with Machine Learning","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129752473","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Structured Text Representations","authors":"J. Zizka, F. Dařena, Arnošt Svoboda","doi":"10.1201/9780429469275-3","DOIUrl":"https://doi.org/10.1201/9780429469275-3","url":null,"abstract":"","PeriodicalId":258194,"journal":{"name":"Text Mining with Machine Learning","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128695315","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Introduction to Text Mining with Machine Learning","authors":"J. Zizka, F. Dařena, Arnošt Svoboda","doi":"10.1201/9780429469275-1","DOIUrl":"https://doi.org/10.1201/9780429469275-1","url":null,"abstract":"","PeriodicalId":258194,"journal":{"name":"Text Mining with Machine Learning","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115551559","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：