January 2014
Beginner to intermediate
860 pages
20h 37m
Japanese
Content preview from Rクイックリファレンス 第2版
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548
20 章 回帰モデル
引数 説明 既定値
entropy
エントロピー
/
最大条件つき尤度フィッティングを使うかど
うかを指定する論理値。
FALSE
softmax
ソフトマックス
/
対数線形モデルと最大条件つき尤度フィッ
ティングを使うかどうかを指定する論理値。
FALSE
censored
入力データを打ち切りデータとして扱うかどうかを指定する
論理値（デフォルトでは、反応変数値
c(1, 0, 1)
は「クラス
1
と
3
の両方」を意味する。データを打ち切りデータとして扱
う場合、
c(1, 0, 1)
は「
2
ではないが、おそらく
1
または
3
」と
いう意味に解釈される）。
FALSE
skip
入力から出力への直結を追加するかどうかを指定する論理値。
FALSE
rang
初期ランダム重みの範囲を指定する数値。
0.7
decay
重み減衰のための数値パラメータ。
0
maxit
最大反復回数。
100
Hess
フィットのヘッセ行列を返すかどうかを指定する論理値。
FALSE
trace nnet
実行中に「トレース（足跡）」を出力するかどうかを指定
する数値。
TRUE
maxNWts
重みの最大数を指定する数値。
1000
abstol
絶対許容範囲を指定する数値（フィット基準が
abstol
未満に
なったらフィッティング過程を停止する）。
1.0e-4
reltol
相対許容範囲を指定する数値（アルゴリズムが各ステップで
誤差を
reltol
だけ減らせない場合はフィッティング過程を停
止する）。
1.0e-8
ニューラルネットワークモデルの最適な重みを見つける簡単な閉形式解はありませ ...
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O’Reilly covers everything we've got, with content to help us build a world-class technology community, upgrade the capabilities and competencies of our teams, and improve overall team performance as well as their engagement.Julian F.
I wanted to learn C and C++, but it didn't click for me until I picked up an O'Reilly book. When I went on the O’Reilly platform, I was astonished to find all the books there, plus live events and sandboxes so you could play around with the technology.Addison B.
I’ve been on the O’Reilly platform for more than eight years. I use a couple of learning platforms, but I'm on O'Reilly more than anybody else. When you're there, you start learning. I'm never disappointed.Amir M.
I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.