Prediction of Domain Values: High throughput screening of domain names using Support Vector Machines
نویسندگان
چکیده
As connected devices multiply and the internet matures into a ubiquitous platform for exchange and communication, the question of what makes a domain name valuable is ever more significant. Due to the scarcity of meaningful vocabulary and the persistence of domain-related data, the buying and selling of previously owned domain names, also known as the domain aftermarket, has evolved into a billion dollar industry. Each day over a 100,000 domain names expire and become available for re-registration. Manual appraisal is impossible at such a volume; thus a method for the automated identification of valuable domain names is called for. The aim of our study was to develop a method for high throughput screening of domain names for rapid identification of the valuable ones. Five different aspects that make a domain name valuable were identified: name quality, domain authority, domain traffic, active domain age and domain health. An SVM method was developed for high throughput screening of domain names. Our method was able to identify valuable domain names with 97% accuracy for the test set and 93% for the external set and can be used for routinely screening the domain aftermarket.
منابع مشابه
A Comparative Study of Extreme Learning Machines and Support Vector Machines in Prediction of Sediment Transport in Open Channels
The limiting velocity in open channels to prevent long-term sedimentation is predicted in this paper using a powerful soft computing technique known as Extreme Learning Machines (ELM). The ELM is a single Layer Feed-forward Neural Network (SLFNN) with a high level of training speed. The dimensionless parameter of limiting velocity which is known as the densimetric Froude number (Fr) is predicte...
متن کاملApplication of Artificial Neural Networks and Support Vector Machines for carbonate pores size estimation from 3D seismic data
This paper proposes a method for the prediction of pore size values in hydrocarbon reservoirs using 3D seismic data. To this end, an actual carbonate oil field in the south-western part ofIranwas selected. Taking real geological conditions into account, different models of reservoir were constructed for a range of viable pore size values. Seismic surveying was performed next on these models. F...
متن کاملA Domain-Based Frequency Count Approach for Protein-Protein Interaction Prediction using Support Vector Machine
Proteins are involved in many essential processes within cell. Uncovering the diverse function of proteins and their interactions within the cell may improve our understanding of protein functions. Several high-throughput techniques employed to decipher PPI are erroneous and are limited by the lack of coverage. Computational techniques are therefore sought to predict genome-wide PPI. In this pa...
متن کاملIn silico prediction of aqueous solubility – classification models
Solubility is a very important parameter in pharmaceutical research, especially for the early phase of drug discovery in fully automatized high throughput screening, compound pool extension and SAR and ADME-Tox parameter measurement. In recent years a multitude of models has been published concerned with the exact prediction of aqueous solubility. Still, almost all in the meantime commercially ...
متن کاملA prediction distribution of atmospheric pollutants using support vector machines, discriminant analysis and mapping tools (Case study: Tunisia)
Monitoring and controlling air quality parameters form an important subject of atmospheric and environmental research today due to the health impacts caused by the different pollutants present in the urban areas. The support vector machine (SVM), as a supervised learning analysis method, is considered an effective statistical tool for the prediction and analysis of air quality. The work present...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید
ثبت ناماگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید
ورودعنوان ژورنال:
- CoRR
دوره abs/1707.00906 شماره
صفحات -
تاریخ انتشار 2017