Sentence examples for manually labeled the from inspiring English sources

We manually labeled the corresponding ground-truth image (Figure4b) using ArcGIS software with five classes, namely rice 1, rice 2, rice 3, rice 4 and rice 5, according to different growth periods after investigation by the author.

We manually labeled the sentiment of a small subset of these documents, and translated into the appropriate language the generic sentiment lexicon used in Case Study One for implementation in this study (using the translation tool available at http://translate.google.com).com

We manually labeled the sentiment of a small subset of these documents, and also translated into Indonesian the generic sentiment lexicon used in Case Study One (in this paper all language translation was performed using the tool available at http://translate.google.com).com

In order to validate the proposed vision-based vehicle detection system as an extended source for FCD applications we have recorded several video sequences in real traffic conditions, and we have manually labeled the number of vehicles in range at every frame (a total of 800 frames).

He then manually labeled the data by following a set of rules such as: duplicate and near duplicate reviews are labeled as spam, reviews about brands only are considered as spam, and non-reviews such as ads, discussions, or irrelevant reviews are labeled as spam.

Using Action W-2 software from AMI, we manually labeled the bad bins (when the subjects took off the instrument; Figure  1a, colored in purple).

We partition each of the 90 fingerprint images into nonoverlapping blocks with the same size of, and manually label the foreground blocks and background blocks.

The best performing teams could reach a performance close to what could be expected by chemical database curators when manually labeling the text.

The CLM can be estimated in a supervised learning manner, i.e., with manually labeling the human objects' correspondence from given training data in advance; or an unsupervised learning manner, i.e., without manually labeling the human objects' correspondence from given training data.

Let us visualize the various approaches in Fig. 1: Scenario A shows typical unsupervised ML: the algorithm is applied on the raw data and learns fully automatic, because it does not require a human to manually label the data.

One trained phonetician manually labelled the following landmarks:  Onset and offset of the neutral lead-in vowel (preceding the target word)  Onset of first stop (V1) burst, if present  Onset and offset of the first target vowel (V1)  Onset of the second stop (C2) burst, if present  Onset and offset of the second target vowel (V2).