News classification
Contact us
- Add: No. 9, North Fourth Ring Road, Haidian District, Beijing. It mainly includes face recognition, living detection, ID card recognition, bank card recognition, business card recognition, license plate recognition, OCR recognition, and intelligent recognition technology.
- Tel: 13146317170 廖经理
- Fax:
- Email: 398017534@qq.com
Artificial intelligence AI conclusion
Artificial intelligence AI conclusion
Business Modeling: Understand business issues, understand project goals and requirements, translate goals into problem definitions, and design a preliminary plan for the purpose. Reasonable hypotheses based on intuition and learning, such as analogy correlation. Difficulties: How to design a reasonable purpose function so that the business initial design request can be reached.
Collect data: collect preliminary data and stop all kinds of familiar data activities. Including data portrayal, data exploration and data quality research. Have data, and do need enough data. Difficulties: 1. How to deal with the problem of data collection, or how to automatically collect data. The need to collect a few data is enough, the academic community has not yet had a fixed theoretical guidance, and the experience formula is being refined from the victory case.
Prepare data: Improve data quality and structure the original raw data into a data set that is ultimately disposed of by the appropriate modeling tool. Includes table, record and attribute selection, data conversion (dense, heterogeneous) and data clearing (missing, contradiction). Difficulties: The specification of quality data is to be analyzed.
Modeling profiling: Select and apply various modeling techniques and stop optimizing their parameters. Ordinary, in order to imitate the performance of unknown data, the data set is often divided into two parts, one for exercise and one for prediction. Difficulties: It is based on how to choose the algorithm and parameters. At present, the choice is based on the analogy method, looking for a successful project similar to the pending project, and using a similar approach, but the project is similar without a uniform specification. Regarding the choice of parameters, the current common method is to experiment as much as possible and select the best parameters for the test results.
Model evaluation: A more thorough evaluation of the model is stopped, and each step of building the model is checked to see if it can actually accomplish the intended purpose. Difficulties: At present, we have not seen the reason for the poor positioning of the reasons, only detailed case analysis.
Similarly, according to the general model-based machine learning, the modeling process is
Collect data exercise and evaluation models.
Collect knowledge to assist in making appropriate model assumptions.
Visualize the data to better understand it, examine data problems and gain insight into useful model assumptions.
Build a model to match the problem domain's knowledge and ensure that the data is divided.
The variables that perform the reasoning to predict use the data to affirm the values of other variables.
The evaluation results use some evaluation indicators to see if they can meet the victory specifications of the target application.
In the (usual) situation, the first time the system does not fit the victory specification, there are two additional steps:
Diagnose problems and reduce prediction accuracy.
Improve the system, which may mean – refine data, models, visualizations, reasoning or evaluation.
Collect data: collect preliminary data and stop all kinds of familiar data activities. Including data portrayal, data exploration and data quality research. Have data, and do need enough data. Difficulties: 1. How to deal with the problem of data collection, or how to automatically collect data. The need to collect a few data is enough, the academic community has not yet had a fixed theoretical guidance, and the experience formula is being refined from the victory case.
Prepare data: Improve data quality and structure the original raw data into a data set that is ultimately disposed of by the appropriate modeling tool. Includes table, record and attribute selection, data conversion (dense, heterogeneous) and data clearing (missing, contradiction). Difficulties: The specification of quality data is to be analyzed.
Modeling profiling: Select and apply various modeling techniques and stop optimizing their parameters. Ordinary, in order to imitate the performance of unknown data, the data set is often divided into two parts, one for exercise and one for prediction. Difficulties: It is based on how to choose the algorithm and parameters. At present, the choice is based on the analogy method, looking for a successful project similar to the pending project, and using a similar approach, but the project is similar without a uniform specification. Regarding the choice of parameters, the current common method is to experiment as much as possible and select the best parameters for the test results.
Model evaluation: A more thorough evaluation of the model is stopped, and each step of building the model is checked to see if it can actually accomplish the intended purpose. Difficulties: At present, we have not seen the reason for the poor positioning of the reasons, only detailed case analysis.
Similarly, according to the general model-based machine learning, the modeling process is
Collect data exercise and evaluation models.
Collect knowledge to assist in making appropriate model assumptions.
Visualize the data to better understand it, examine data problems and gain insight into useful model assumptions.
Build a model to match the problem domain's knowledge and ensure that the data is divided.
The variables that perform the reasoning to predict use the data to affirm the values of other variables.
The evaluation results use some evaluation indicators to see if they can meet the victory specifications of the target application.
In the (usual) situation, the first time the system does not fit the victory specification, there are two additional steps:
Diagnose problems and reduce prediction accuracy.
Improve the system, which may mean – refine data, models, visualizations, reasoning or evaluation.