Translation of "centroid" to Japanese language:
Dictionary English-Japanese
Examples (External sources, not reviewed)
| Lots of treasure buried in the centroid... centroid... centroid of the mechanism. | たくさんの財宝を セントロイドに埋めた セントロイドは機械仕掛けなんだ |
| Xi and the cluster centroid | これ この |
| The... the centroid of the mechanism, or... | そのセントロイドの仕掛けってのは |
| Buried in the centroid of the mechanism. | 機械仕掛けの セントロイドに埋めたんだ |
| K of the cluster centroid closest to Xl. | つまりこれは クラスタ割り付けステップにあたる |
| And then I'm going to do another move centroid step. | つまり全ての青の点の |
| And the same things for the blue cluster centroid, look at all the blue dots and compute their mean, and then move the blue cluster centroid there. | 行う 青い点を 全て見て 平均を計算し 青のクラスタ重心をそこへ移動する |
| First is a cluster assignment step, and second is a move centroid step. | 二番目は重心移動ステップ それらが何を意味するか解説していこう |
| The other in the loop of K means does the move centroid step. | 他の仕事は重心移動のステップだ |
| Because each of these example x1, x5, x6, x10 each of them were an n dimensional vector, and I'm going to add up these things and, you know, divide by four because I have four points assigned to this cluster centroid, I end up with my move centroid step, for my cluster centroid mu 2. | x1 x5 x6 x10は どれもn次元ベクトルだったから そしてこれらを |
| But what if there is a cluster centroid no points with zero points assigned to it. | 点が0個しか割り振られない クラスタ重心があったら どうしたらいい その場合 一番普通の対応は |
| In that case the more common thing to do is to just eliminate that cluster centroid. | たんにそのクラスタ重心を 取り除く そうすると |
| So we think of Ci as picking the cluster centroid with the smallest squared distance to my training example Xi. | トレーニング手本xiとの二乗距離が 最小になるクラスタ重心を選びとった物と考える事が出来る だがもちろん 距離の二乗を最小化しようと |
| Then in this move centroid step, what I'm going to do is just compute the average of these four things. | そこでやるべきは単に これら4つの平均を取るという事 つまりx1 x5 x6 x10と |
| Specifically, what I mean by that, is to go through your data set and color each of the points either red or blue, depending on whether it is closer to the red cluster centroid or the blue cluster centroid, and I've done that in this diagram here. | データセットを見ていって 各点を赤か青に 色付けしていく |
| Sometimes if you really need k clusters, then the other thing you can do if you have a cluster centroid with no points assigned to it is you can just randomly reinitialize that cluster centroid, but it's more common to just eliminate a cluster if somewhere during | 必要な場合もある その場合にやる別の手段としては もし点が割り振られない クラスタ重心が |
| So colour each point red or blue, based on what it's closer to and then do another move centroid step and we're done. | とちらに近いかに基づいて そして次に 重心移動のステップを行う 行った |
| If we got that from the cluster assignment step, then that means examples 1,5,6 and 10 were assigned to the cluster centroid two. | それを得たとすると それはつまり 手本1 5 6 10は クラスタ重心2が割り付けられている |
| K means it with no points assigned to that cluster centroid, and that can happen, altthough in practice it happens not that often. | 点が割り振られなかったら そしてそもそも実際には そういう事はめったに起こらない |
| And now I'm going to average them so here I have four points assigned to this cluster centroid, just take one quarter of that. | 平均したいのだから このクラスタには点が4つ 割り振られているのだから |
| What that means is that, it's going through each of the examples, each of these green dots shown here and depending on whether it's closer to the red cluster centroid or the blue cluster centroid, it is going to assign each of the data points to one of the two cluster centroids. | 各手本を見ていって ここで示したのだと この緑のドットを 見ていき |
| So this was my cluster assignment step, where we took each of my examples and coloured it either red or blue, depending on which cluster centroid it was closest to. | そこでは各サンプルに対して それを赤か青か そのどちらの重心に近いかに基づいて |
| So as a concrete example, let's say that one of my cluster centroids, let's say cluster centroid two, has training examples, you know, 1, 5, 6, and 10 assigned to it. | クラスタ重心の一つ クラスタ重心2としよう それがトレーニング手本を持ってるとして |
| So that's the distance between the example and the cluster centroid and so what I'm going to do is find the value of K, of lower case k that minimizes this, and so the value of k that minimizes you know, that's what I'm going to set as Ci, and by convention here I've written the distance between Xi and the cluster centroid, by convention people actually tend to write this as the squared distance. | 我らがやるべき事は これを最小化する k このkは小文字のkだが |
| And then we go back to another cluster assignment step, so we're again going to look at all of my unlabeled examples and depending on whether it's closer the red or the blue cluster centroid, | つまりまたラベルづけされていない 手本を全て見ていって 青と赤のどちらの重心に |
| So what we are going to do is look at all the red points and compute the average, really the mean of the location of all the red points, and we are going to move the red cluster centroid there. | 全ての赤の点を見て 平均を計算し それは真に全ての赤い点の |
| But so, Ci is going to, I'm going to minimize over my values of k and find the value of K that minimizes this distance between Xi and the cluster centroid, and then, you know, the value of k that minimizes this, that's what gets set in | kの値に関して最小化する そしてこのxiとクラスタ重心の 距離を最小化する |
| loop of K means, is the move centroid step, and what we are going to do is, we are going to take the two cluster centroids, that is, the red cross and the blue cross, and we are going to move them to the average of the points colored the same colour. | 重心の移動ステップだ 我らがやるべきことは 2つのクラスタの重心を |
