This paper presents a practical algorithm for facial age estimation from frontal face image. Facial age estimation generally comprises two key steps including age image representation and age estimation. The anthropometric model used in this study includes computation of eighteen craniofacial ratios and a new accurate skin wrinkles analysis in the first step and a pairwise binary support vector...

Drug design is the approach of finding drugs by design using computational tools. When designing a new drug, the structure of the drug molecule can be modeled by classification of potential chemical compounds. Kernel Methods have been successfully used in classifying chemical compounds, within which the most popular one is Support Vector Machine (SVM). In order to classify the characteristics o...

The emotion or mood of a user can be detected by their facial expressions. Those expressions extracted from the live feed through system’s camera. Machine learning provides various techniques, one which is detection expression. It connects us across markets, aeons, backgrounds, dialects, political views, and financial status. Nowadays, music applications other streaming services are high demand...

Support vector machine is a part of learning approach based on statistical theory. Due to the higher accuracy values, machines have become focus for frequent users. This paper will introduce basic theory machine, idea classification and algorithm support that be used. Solving problem use an algorithm, prove effectiveness data has been In this study, obtained very good results in its completion....

In view of pollution prediction modeling, the study adopts homogenous (random forest, bagging, and additive regression) and heterogeneous (voting) ensemble classifiers to predict the atmospheric concentration of Sulphur dioxide. For model validation, results were compared against widely known single base classifiers such as support vector machine, multilayer perceptron, linear regression and re...

The existing multi-label support vector machine (Rank-SVM) has an extremely high computational complexity due to a large number of variables in its quadratic programming. When the Frank–Wolfe (FW) method is applied, a large-scale linear programming still needs to be solved at any iteration. Therefore it is highly desirable to design and implement a new efficient SVM-type multi-label algorithm. ...

1 The recently proposed rough margin based support vector machine (RMSVM) could tackle the overfitting problem due to outliers effectively with the help of rough margins. However, the standard solvers for them are time consuming and not feasible for large datasets. On the other hand, the core vector machine (CVM) is an optimization technique based on the minimum enclosing ball that can scale up...

We present a machine-learning based technique for the problem of algorithm selection, specifically focusing on algorithms for dense matrix multiplication (DMM). Dense matrix multiplication is a core part of many highperformance computing and machine learning algorithms, but the performance of DMM algorithms can vary significantly based on their input and the performance characteristics of each ...