Forecast of compensation amount based on big data network and machine learning algorithm in intellectual property law

摘要

Management profit forecasting determines the cash compensation of performance target executives. Agency theory is consistent with the material, and found that the sense of monetary compensation with executive power is different to the extent that the initial expected management of realized income goes higher. Employees are considered an important asset of a business entity. Performance goals are important for both individuals and companies to their satisfaction in doing this work. The retail industry faces similar challenges, positively related to Management Forecast Error (MFE) executive cash compensation and MFE enhancer (weak) Employees' executives present plans to strengthen cash compensation Exceeds current earnings (deficit falls) Relationship, career satisfaction. Another study found that the sensitivity to payback performance very positive and the upper limit of total cash compensation due to MFEs weak. This initial management forecast can be used as a service target in the executive agreement compensation agreement. Compensation forecasting is critical to a company's success; to meet and surpass revenue targets, you need to have an understanding of trends and increasing interest rates will affect your budget. The focus is on bridging the gap between standard and metrological mechanisms up to employee care to categorize. Therefore, this research uses machine learning algorithms (K-algorithms and Simple Vector Machine SVMs) to automatically categorize the management of data personnel from the careful or careless use of the consumer. There are no suitable machine learning mechanisms to manage these research systems. In short, this work can help staff and drive their progress in many areas. Developing and implementing individual learning teaching techniques that help future workers and, potentially capable of, and willing to help people expected to develop.