人工智慧論文編修樣本

人工智慧是研究、開發用於模擬、延伸和擴展人的智慧的理論、方法、技術及應用系統的一門新的技術科學。Enago在人工智慧領域有深厚的專業知識，擁有人工智慧和自動程式設計、機器學習、電腦視覺等相關學科的專業編輯和校對人員，且已編修了大量此領域相關的科研論文，並協助眾多各國學術作者成功在國際知名SCI期刊上發表高水準論文。

編修後的文稿

The proposed age estimation algorithm realisesrealizes hierarchicacal approach (Ffig. 10). First of all 1, the input fragments are divided intofor three age groups: lesssmaller  2 than 18 years old, from 18– - 45 years old, and morebigger than 45 years old. Second,Afterwards the results of this in the first stepage are furthermore subdivided into seven smallernewer groups, with each limiteding to aone single decade. This reduceshus the originalproblem of multiclass classification problemis therefore reduced to a set of binary “‘one-against-all”’ classifiers (; BCs). EachThese classifiers calculate: then ranks theof imageseach based onof the aassociatednalyzed class, and. tThe finaltotal decisions areis obtained then by the analyzsings these previously received rank histograms of ranks.  3

A two-level-schemes of These binary classifierBCs are constructed using a two-level approach. After ion is applied firstwith the transitioning to an adaptive feature space, as equal to this described earlier, the images are, classified usingand support vector machiness classification with radial basis functionRBF kernels.

The iInput fragments arewere preprocessed for their luminance characteristics to align and to transform 4 their luminance characteristicsthem to a uniformal scale. This pPreprocessing step includes color-space transformation and scaling, both operations similar to those used in thethat of a gender recognition algorithm. Features, are calculated for each colour component and, are combined to form a uniform featureed vector. 5

Training and testing require a sufficientlyhuge largeenough coloring image database.: Here, wWe combinedused the state-of-the-art image databases MORPH and FG-NET image databases with our own image database, gathered from many different sources and, which comprisinged of 10,500 face images. The fFaces ion the images were detected automatically by the AdaBoost face detection algorithms.

A total number of seven thousand7000 images were used to train and test the first stage of thefor age classification algorithm training and testing on the first stage. Three3 binary classifierBCs were created, eachmade withutilizing 144 adaptive features each of.

The first-stage cClassification results showedon the first stage are: 82 % accuracy for young facesage, 58 % accuracy for middle- aged faces, and 92 % accuracy for elderly facessenior age. The overall aAge classification accuracy forrate in thea three age categories wasdivision problem – 77.3 %.

The second-stage BCBinary classifiers of the second stage were constructed in the same way as forequal to the first stage ( described above). Fig. 11 shows aA visual example of age estimation by the first stage of the proposed algorithm on its first stage is presented in figs. 11.

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編修後的文稿

The age estimation algorithm realisesrealizes hierarchicacal approach (fig. 10). First of all 1, the input fragments are divided intofor three age groups: lesssmaller than 18 years old, from 18– - 45 years old, and morebigger than 45 years old. Second,Afterwards the results of this in the first stepage are more subdivided into seven smallernewer groups, with each limiteding to one single decade. Thus, the problem of multiclass classification is therefore reduced to a set of binary “‘one-against-all”’ classifiers (; BCs). These classifiers calculate: then ranks theof imageseach based onof the aassociatednalyzed class, and. tThe finaltotal decisions areis obtained then by the analyzsings these previously received rank histograms of ranks.  2

These BCs are constructed using a two-level approach. After ion is applied firstwith the transitioning to an adaptive feature space, as equal to this described earlier, the images are, classified usingand support vector machiness classification with RBF kernels.

The iInput fragments arewere preprocessed for their luminance characteristics to align and to transform them to a uniformal scale. This pPreprocessing step includes color-space transformation and scaling, both operations similar to those used in thethat of a gender recognition algorithm. Features, are calculated for each colour component and, are combined to form a uniform featureed vector. 3

Training and testing require a sufficientlyhuge largeenough coloring image database.: We combinedused the state-of-the-art image databases MORPH and FG-NET image databases with our own image database, gathered obtained from many different sources and, which comprisinged of 10,500 face images. The fFaces ion the images were detected automatically by the AdaBoost face detection algorithms.

A total number of seven thousand7000 images were used to train and test the first stage of thefor age classification algorithm training and testing on the first stage. Three3 binary classifierBCs were createdmade utilizing 144 adaptive features each of.

The first-stage cClassification results showedon the first stage are: 82 % accuracy for young facesage, 58 % accuracy for middle- aged faces, and 92 % accuracy for elderly facessenior age. The overall aAge classification accuracy forrate in thea three age categories wasdivision problem – 77.3 %.

The second-stage BCBinary classifiers of the second stage were constructed in the same way as forequal to the first stage ( described above). Fig. 11 shows aA visual example of age estimation by the first stage of the proposed algorithm on its first stage is presented in figs. 11.

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