Hybrid search: Enhancing search efficiency through the combination of different techniques.
Hybrid search is an approach that combines multiple search techniques to improve the efficiency and effectiveness of search algorithms, particularly in complex and high-dimensional spaces. By integrating various methods, hybrid search can overcome the limitations of individual techniques and adapt to diverse data distributions and problem domains.
In the context of machine learning, hybrid search has been applied to various tasks, such as path planning for autonomous vehicles, systematic literature reviews, and model quantization for deep neural networks. These applications demonstrate the potential of hybrid search in addressing complex problems and enhancing the performance of machine learning algorithms.
One example of hybrid search in machine learning is the Roadmap Hybrid A* and Waypoints Hybrid A* algorithms for path planning in industrial environments with narrow corridors. These algorithms combine Hybrid A* with graph search and topological maps, respectively, to improve computational speed, robustness, and flexibility in navigating obstacles and generating optimal paths for car-like autonomous vehicles.
Another application is the use of hybrid search strategies for systematic literature reviews in software engineering. By combining database searches in digital libraries with snowballing techniques, researchers can achieve a balance between result quality and review effort, leading to more accurate and comprehensive reviews.
In the field of deep neural network compression, hybrid search has been employed to automatically realize low-bit hybrid quantization of neural networks through meta learning. By using a genetic algorithm to search for the best hybrid quantization policy, researchers can achieve better performance and compression efficiency compared to uniform bitwidth quantization.
A company case study that demonstrates the practical application of hybrid search is the development of Hybrid LSH, a technique for faster near neighbors reporting in high-dimensional space. By integrating an auxiliary data structure into LSH hash tables, the hybrid search strategy can efficiently estimate the computational cost of LSH-based search for a given query, allowing for better performance across a wide range of search radii and data distributions.
In conclusion, hybrid search offers a promising approach to enhance the efficiency and effectiveness of search algorithms in machine learning and other domains. By combining different techniques and adapting to diverse problem contexts, hybrid search can lead to improved performance and more accurate results, ultimately benefiting a wide range of applications and industries.
Hybrid search Further Reading1.Improved path planning algorithms for non-holonomic autonomous vehicles in industrial environments with narrow corridors: Roadmap Hybrid A* and Waypoints Hybrid B*. Roadmap hybrid A* and Waypoints hybrid A* Pseudocodes http://arxiv.org/abs/2304.14043v1 Alessandro Bonetti, Simone Guidetti, Lorenzo Sabattini2.Proceedings 6th International Workshop on Local Search Techniques in Constraint Satisfaction http://arxiv.org/abs/0910.1404v1 Yves Deville, Christine Solnon3.On the Performance of Hybrid Search Strategies for Systematic Literature Reviews in Software Engineering http://arxiv.org/abs/2004.09741v1 Erica Mourão, João Felipe Pimentel, Leonardo Murta, Marcos Kalinowski, Emilia Mendes, Claes Wohlin4.Combined Global and Local Search for the Falsification of Hybrid Systems http://arxiv.org/abs/1406.6332v1 Jan Kuřátko, Stefan Ratschan5.Broadcast Search in Innovation Contests: Case for Hybrid Models http://arxiv.org/abs/1204.3343v1 Thomas Gegenhuber, Marko Hrelja6.Hybrid Quantum-Classical Search Algorithms http://arxiv.org/abs/2202.11443v1 Ansis Rosmanis7.Automatic low-bit hybrid quantization of neural networks through meta learning http://arxiv.org/abs/2004.11506v1 Tao Wang, Junsong Wang, Chang Xu, Chao Xue8.Hybrid LSH: Faster Near Neighbors Reporting in High-dimensional Space http://arxiv.org/abs/1607.06179v3 Ninh Pham9.Efficient hybrid search algorithm on ordered datasets http://arxiv.org/abs/1708.00964v1 Adnan Saher Mohammed, Şahin Emrah Amrahov, Fatih V. Çelebi10.Exotic mesons: status and future http://arxiv.org/abs/0711.3518v1 Eberhard Klempt
Hybrid search Frequently Asked Questions
How does hybrid search work?
Hybrid search works by combining multiple search techniques to improve the efficiency and effectiveness of search algorithms, particularly in complex and high-dimensional spaces. By integrating various methods, hybrid search can overcome the limitations of individual techniques and adapt to diverse data distributions and problem domains. This approach can be applied to various tasks in machine learning, such as path planning for autonomous vehicles, systematic literature reviews, and model quantization for deep neural networks.
What is a hybrid search engine?
A hybrid search engine is a search system that combines different search techniques, algorithms, or data structures to improve search efficiency and effectiveness. By leveraging the strengths of multiple methods, a hybrid search engine can provide more accurate and relevant results, adapt to diverse data distributions, and handle complex problem domains. Hybrid search engines can be used in various fields, including machine learning, information retrieval, and database management.
Why is Google considered a hybrid search engine?
Google is considered a hybrid search engine because it combines various search techniques and algorithms to provide more accurate and relevant search results. Google's search engine uses a combination of keyword-based search, natural language processing, machine learning, and other techniques to understand user queries, rank web pages, and deliver the most relevant results. This combination of methods allows Google to adapt to diverse data distributions and handle complex search tasks, making it a powerful and efficient search engine.
How does Pinecone search work?
Pinecone is a vector search engine designed for machine learning applications. It works by converting data points into high-dimensional vectors and then indexing these vectors for efficient similarity search. Pinecone uses advanced algorithms and data structures, such as approximate nearest neighbor (ANN) search, to enable fast and accurate search in high-dimensional spaces. This approach is particularly useful for tasks like recommendation systems, image search, and natural language processing, where traditional search methods may struggle with the complexity and scale of the data.
What are the benefits of using hybrid search in machine learning?
Hybrid search offers several benefits in machine learning, including improved efficiency, effectiveness, and adaptability. By combining different search techniques, hybrid search can overcome the limitations of individual methods and better handle complex problems and high-dimensional spaces. This can lead to improved performance, more accurate results, and the ability to adapt to diverse data distributions and problem domains. Hybrid search can be applied to various machine learning tasks, such as path planning, literature reviews, and neural network compression.
Can hybrid search be applied to other fields besides machine learning?
Yes, hybrid search can be applied to other fields besides machine learning, such as information retrieval, database management, and optimization problems. By combining different search techniques and algorithms, hybrid search can improve the efficiency and effectiveness of search tasks in various domains. This can lead to better performance, more accurate results, and the ability to adapt to diverse problem contexts and data distributions, ultimately benefiting a wide range of applications and industries.
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