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BEIJING, Oct. 19, 2023 /PRNewswire/ — WiMi Hologram Cloud Inc. (NASDAQ: WIMI) (“WiMi” or the “Company”), a leading global provider of augmented truth (“AR”) technologies for holograms, announced that its R team
The Synthetic Intelligence Device Learning-Based Multi-View Merge Ruleset is a ruleset that uses a device learning strategy for co-learning and merging multiple perspectives received from other viewpoints or data sources. The device learning ruleset was carried out to a greater extent in many PC vision and symbol processing responsibilities due to the strong functionality demonstrated in classification, feature extraction, knowledge representation, and other issues. In the multi-view merge ruleset, we can mix features from other perspectives to get more complete and accurate data. Information from other perspectives can also be merged to achieve research accuracy and knowledge forecasting, in addition to their ability to deal with multiple types of knowledge at the same time, making it less difficult to leverage forward-looking insights. The set of rules studied through WiMi typically includes steps such as knowledge preprocessing, multi-view merging, feature learning, style training, and prediction.
Data preprocessing: Data preprocessing is the first step in the multi-view ruleset and is used to ensure the quality and consistency of knowledge. Data preprocessing for the view includes steps such as knowledge cleanup, function selection, function extraction, and knowledge normalization. The steps aim to cut through noise, decrease redundant information, and extract features that are vital to the performance of the ruleset.
Multi-view fusion: The pre-processed multiple perspectives are then merged. Fusion can be undeniable weighted averaging or a more complex approach to integrating models such as neural networks. By merging data from other perspectives, the benefits of other perspectives can be leveraged. synthesized to the functionality of the algorithm.
Learning Functions and Learning Representations: Learning functions and learning representations are very vital steps in the multi-view algorithm. With the learned functions and representations, the patterns and structures hidden in knowledge can be better captured, the accuracy and generalization of the algorithm. Commonly used function learning strategies come with core component analysis, auto-encoder, and more.
Model Education and Prediction: Machine information models are trained to know the correlation between multi-view knowledge, knowledge that has gone through feature education, and representational information. Commonly used device information models come with SVMs, resolution trees, deep neural networks, and more. Models received through education can be used for prediction and classification tasks; For example, new incoming insights can be predicted and evaluated by trained models.
The multi-view merging ruleset based on synthetic intelligence device learning has technical benefits such as knowledge richness, information complementarity, style fusion capability, and adaptability, giving the multi-view ruleset excellent insights and application costs to handle. Complex, multi-source problems. Knowledge analysis.
Each view in multi-view knowledge provides other varied types of knowledge, such as text, images, sounds, and so on, and each type of knowledge has its unique characteristics and representations, and this data can complement each other. By merging data from other views, more complete and accurate feature representations can be received, and the functionality of knowledge research and style education can be improved, and more accurate and comprehensive effects can be received to perceive and analyze the challenge more holistically. By merging styles from other views, more complex styling functions can be achieved and the overall functionality of the style can be improved.
In addition to this, the multi-view merge ruleset can better manage noise and anomalies in information by using data from multiple perspectives, reducing interference in a single view, and improving the robustness of the ruleset against others. noise and anomalous knowledge. You can also adaptively choose appropriate perspectives and models for education and prediction based on other responsibilities and characteristics of knowledge, and this adaptability can be the adaptability and generalization of the set of rules.
The multi-view merging rule set has a wide diversity of programs in symbol processing, virtual marketing, social media, and IoT boxes. By gathering insights from other perspectives and merging them, advertising recommendations and intelligent programs can be formulated more appropriately. In the field of virtual marketing, the multi-view fusion rule set can use multiple perspectives of user behavior, user characteristics, item characteristics, etc. , and synthesize multiple data to obtain better virtual marketing effectiveness. For example, knowledge of user behavior, knowledge of user profile, and knowledge of item characteristics can be combined to improve the accuracy and personalization of responsibilities such as personalized recommendations, advertising recommendations, or data filtering. In the IoT box, the multi-view fusion rule set can be used in smart homes and smart cities, where the control of smart homes and smart cities can be best achieved through the collection of sensor knowledge, environmental knowledge and the user’s knowledge from other points of view and merge this knowledge. In the field of symbol processing, the multi-view fusion rule set can use multiple perspectives received from other sensors, cameras or symbol processing techniques, and synthesize multiple data to obtain better symbol processing. image. For example, symbols from other spectra, resolutions, or angles can be merged to improve the quality of a symbol, decorate key issues, and improve the functionality of responsibilities such as classification or target detection.
With the progression of large knowledge generation and synthetic intelligence, WiMi will integrate deep neural networks, multimodal learning, and other technologies in the long term to frequently advertise the technological innovation of the multi-view fusion algorithm, integrate deep neural networks and other technologies more comprehensively. , performs deep feature extraction and knowledge fusion from multiple views, and improves the execution and effect of the algorithm. It also conducts the effective fusion and investigation of other modal knowledge.
About WIMI Hologram Cloud
WIMI Hologram Cloud, Inc. (NASDAQ: WIMI) is a full-service cloud holographic technical solutions provider focusing on professional boxes, adding holographic AR automotive HUD software, 3D holographic pulse LiDAR, head-mounted lightbox holographic equipment, semiconductor holographic equipment, cloud holographic software, automotive holographic navigation, and others. Its holographic AR and technologies come with holographic AR automotive application, three-dimensional holographic pulse LiDAR technology, holographic vision semiconductor technology, holographic software development, holographic AR advertising technology, holographic AR entertainment technology, holographic ARSDK payment, interactive holographic communication and other holographic technologies. AR technologies.
Safe Harbor Declarations
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Additional data related to these and other perils are included in the Company’s Annual Report on Form 20-F and the Current Report on Form 6-K and other filings with the SEC. All data provided in this press release are as of the date of this press release. launch. The Company assumes no legal responsibility to update any forward-looking statements, as required by applicable law.
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