Home Technology peripherals AI Five schools of machine learning you don't know about

Five schools of machine learning you don't know about

Jun 05, 2024 pm 08:51 PM
AI machine learning

Machine learning is an important branch of artificial intelligence that gives computers the ability to learn from data and improve their capabilities without being explicitly programmed. Machine learning has a wide range of applications in various fields, from image recognition and natural language processing to recommendation systems and fraud detection, and it is changing the way we live. There are many different methods and theories in the field of machine learning, among which the five most influential methods are called the "Five Schools of Machine Learning". The five major schools are the symbolic school, the connectionist school, the evolutionary school, the Bayesian school and the analogy school.

Five schools of machine learning you dont know about

1.Symbolism

Symbolism, and Known as symbolism, it emphasizes the use of symbols for logical reasoning and expression of knowledge. This school of thought believes that learning is a process of reverse deduction, using existing knowledge and rules to seek insights from philosophy, psychology and logic. The origins of semiotics can be traced back to ancient times, when early philosophers, logicians, and psychologists studied cognition through the use of symbols. However, truly systematic semiotics began in French culture in the late 19th and early 20th centuries, promoted primarily by a group of writers, artists, and philosophers

  • representative Character

Herbert Simon: One of the founders of the semiotic school, he and Allen Newell jointly proposed the universal Concept of Problem Solver (GPS).

Allen Newell: One of the founders of the semiotic school, he and Herbert Simon proposed the universal problem solver ( GPS) concept.

John McCarthy: John McCarthy is one of the pioneers in the field of artificial intelligence and a representative of the semiotic school. He proposed the term "artificial intelligence" in 1956 and developed the LISP programming language, which became an important tool for symbolism research. McCarthy's work mainly focused on logical reasoning and knowledge representation, and he believed that computers could simulate human thinking processes through symbols.

Marvin Lee Minsky: One of the founders of the MIT Artificial Intelligence Laboratory. He proposed the framework theory and made contributions to the field of artificial intelligence. made significant contributions. Marvin Minsky is a leading computer scientist and cognitive scientist. He began studying artificial intelligence in the 1950s and became one of the pioneers in the field. His research focus

  • Main algorithm

Inductive Logic Programming (ILP) is a kind of reverse Methods of reasoning. Abverse reasoning usually uses logical reasoning to discover knowledge by extracting general rules from specific examples.

2. Connectionism School

Connectionism, also known as connectionism, is inspired by neural Science and physics, emphasizing reverse engineering of the brain and simulating the structure and function of neural networks. This school of thought believes that intelligence arises through the connections and interactions between large numbers of simple units (neurons). The theory holds that the simulation of connections and interactions between neurons can produce intelligent behavior. This connection and interaction is achieved through connections between simple units (neurons). By adjusting the strength and weight of connections in a neural network, the connections and information transfer between neurons in the human brain can be simulated. One of the main advantages of connectionology is that it allows the generation of intelligence through a large number of simple units Yann LeCun is a brilliant scientist who developed convolutional neural networks and successfully applied them to computer vision tasks such as handwritten digit recognition. LeCun's work has greatly promoted the development of deep learning in practical applications.

  • Geoffrey Hinton: A pioneer of deep learning, he proposed important architectures such as convolutional neural networks (CNN) and deep belief networks (DBN).
Yoshua Bengio: A pioneer of deep learning, he proposed important architectures such as the Long Short-Term Memory (LSTM) network.

David Rumelhart: Psychologist, one of the founders of the Parallel Distributed Processing (PDP) model, he proposed the backpropagation algorithm .

Frank Rosenblatt: Psychologist and inventor of the perceptron. He proposed the perceptron learning algorithm.

  • Main algorithm

The main algorithm of the connection school is Backpropagation. Backpropagation is an algorithm that updates the weight of a neural network by calculating the gradient of the loss function, which greatly improves the efficiency of training deep neural networks.

3. Evolutionary Computation

Evolutionary Computation is inspired by genetics and evolutionary biology. Learn and optimize by simulating biological evolution processes. The core idea of ​​this school is to use genetic operations such as selection, crossover and mutation to simulate the biological evolution process on the computer to find the optimal solution to the problem.

  • Representative figure

John Holland

John Holland is a pioneer in the field of evolutionary computing. He proposed the genetic algorithm (Genetic Algorithm) in the 1960s. Holland's work laid the foundation for evolutionary computation, and his genetic algorithms used natural selection and genetic operations to solve complex optimization problems.

David E.Goldberg

David Goldberg’s research and development on genetic algorithms Made important contributions to applications. His book "Genetic Algorithm" introduces the theory and application of genetic algorithms in detail, which has attracted widespread attention and development in this field.

  • Main Algorithm

The main algorithm of the evolutionary school is Genetic Programming (GP). Genetic programming is an algorithm that uses evolutionary computing technology to automatically generate computer programs. It gradually optimizes the program to solve specific problems by simulating the biological evolution process.

4. Bayesian school

Bayesianism is based on statistics and believes that Learning is a process of probabilistic reasoning. This school of thought utilizes Bayes' theorem to perform learning and inference by updating the prior probability distribution.

  • Representative figure

Thomas Bayes

Thomas Bayes was a British mathematician. The Bayes theorem he proposed became the basis of Bayesian reasoning. Although Bayes himself was not directly involved in machine learning research, his work was of great significance to the formation and development of the Bayesian school.

Judea Pearl

Judea Pearl works on Bayesian networks and causal inference Made outstanding contributions. His development of Bayesian networks is an important tool that makes probabilistic reasoning in complex systems more efficient and intuitive. Pearl's work has had a profound impact on both artificial intelligence and statistics.

  • Main algorithm

The main algorithm of the Bayesian school is Bayesian Inference. Bayesian reasoning makes predictions and decisions by calculating posterior probabilities, and has significant advantages in dealing with uncertainty and complex systems.

5. School of Analogy

The school of analogy (Analogism) learns by extrapolating similarity judgments , influenced by psychology and mathematical optimization. This school emphasizes analogical reasoning from known examples to discover new knowledge and solve problems.

  • Representative figure

Vladimir Vapnik

Vladimir Vapnik is one of the important representatives of the analogy school. He and Alexey Chervonenkis jointly proposed the Support Vector Machine (SVM) ). Support vector machine is a supervised learning method based on statistical learning theory and is widely used in classification and regression problems.

Tom Michael Mitchell

Tom Michael Mitchell has made extensive contributions in the field of machine learning , his book "Machine Learning" is an important textbook in this field. Kowalski's research on analogy learning and inductive logic programming provided important theoretical support for the development of the analogy school.

  • Main algorithm

#The main algorithm of the analogy school is Support Vector Machine (SVM). Support vector machines implement classification tasks by constructing a hyperplane to maximize the separation between different categories. In high-dimensional data spaces, SVM performs well and is especially suitable for complex pattern recognition problems.

6. Comparison of the five major schools of machine learning

school

Representative

Main idea

##Main algorithm

Application fields

Semiotic School

Herbert Simon, Allen Newell, John McCarthy, Marvin Lee Minsky

Learning is a process of symbol manipulation

Reverse deduction

Knowledge representation, natural language processing

Connection School

Yon LeCun, Jeffrey Hinton, Joshua Bengio, David Rummelhart, Frank Rosenblatt

Learning is a process that simulates the brain’s neural network

Backpropagation

Image recognition, speech recognition, natural language processing

Evolutionary School

John Holland, David Goldberg

Learning is a simulation The process of biological evolution

Genetic algorithm, evolutionary strategy

Robot control and optimization problems Solving

Bayesian

Thomas Bay Yes, Judea Pearl

Learning is a process of probabilistic reasoning

Bayes Theorem

##Spam filtering, medical diagnosis, information retrieval

School of Analogy

Vladimir Vapnik, Tom Michael Mitchell

Learning is a Process by extrapolation of similarity judgments

#Learning algorithm based on analogy

Recommendation system, case reasoning, machine translation

7. Summary

The five major schools of machine learning have their own characteristics, starting from different perspectives and theoretical foundations to solve various complex learning problems. The semiotic school emphasizes logical reasoning and knowledge representation, the connectionist school simulates the structure and function of neural networks, the evolutionary school uses the biological evolution process for optimization, the Bayesian school handles uncertainty through probabilistic reasoning, and the analogical school performs analogical reasoning through similarity judgments. . Each school has its representatives and main algorithms, and their contributions jointly promote the development and progress of the field of machine learning.

Although these five schools have differences in theories and methods, they are not mutually exclusive, but can complement and integrate. In practical applications, researchers often combine multiple methods to deal with complex and changeable problems. With the development of technology and the deepening of interdisciplinary research, machine learning will continue to play an important role in all aspects of artificial intelligence, bringing more innovations and breakthroughs.

The above is the detailed content of Five schools of machine learning you don't know about. For more information, please follow other related articles on the PHP Chinese website!

Statement of this Website
The content of this article is voluntarily contributed by netizens, and the copyright belongs to the original author. This site does not assume corresponding legal responsibility. If you find any content suspected of plagiarism or infringement, please contact admin@php.cn

Hot AI Tools

Undresser.AI Undress

Undresser.AI Undress

AI-powered app for creating realistic nude photos

AI Clothes Remover

AI Clothes Remover

Online AI tool for removing clothes from photos.

Undress AI Tool

Undress AI Tool

Undress images for free

Clothoff.io

Clothoff.io

AI clothes remover

AI Hentai Generator

AI Hentai Generator

Generate AI Hentai for free.

Hot Article

R.E.P.O. Energy Crystals Explained and What They Do (Yellow Crystal)
2 weeks ago By 尊渡假赌尊渡假赌尊渡假赌
Repo: How To Revive Teammates
4 weeks ago By 尊渡假赌尊渡假赌尊渡假赌
Hello Kitty Island Adventure: How To Get Giant Seeds
3 weeks ago By 尊渡假赌尊渡假赌尊渡假赌

Hot Tools

Notepad++7.3.1

Notepad++7.3.1

Easy-to-use and free code editor

SublimeText3 Chinese version

SublimeText3 Chinese version

Chinese version, very easy to use

Zend Studio 13.0.1

Zend Studio 13.0.1

Powerful PHP integrated development environment

Dreamweaver CS6

Dreamweaver CS6

Visual web development tools

SublimeText3 Mac version

SublimeText3 Mac version

God-level code editing software (SublimeText3)

Bytedance Cutting launches SVIP super membership: 499 yuan for continuous annual subscription, providing a variety of AI functions Bytedance Cutting launches SVIP super membership: 499 yuan for continuous annual subscription, providing a variety of AI functions Jun 28, 2024 am 03:51 AM

This site reported on June 27 that Jianying is a video editing software developed by FaceMeng Technology, a subsidiary of ByteDance. It relies on the Douyin platform and basically produces short video content for users of the platform. It is compatible with iOS, Android, and Windows. , MacOS and other operating systems. Jianying officially announced the upgrade of its membership system and launched a new SVIP, which includes a variety of AI black technologies, such as intelligent translation, intelligent highlighting, intelligent packaging, digital human synthesis, etc. In terms of price, the monthly fee for clipping SVIP is 79 yuan, the annual fee is 599 yuan (note on this site: equivalent to 49.9 yuan per month), the continuous monthly subscription is 59 yuan per month, and the continuous annual subscription is 499 yuan per year (equivalent to 41.6 yuan per month) . In addition, the cut official also stated that in order to improve the user experience, those who have subscribed to the original VIP

Context-augmented AI coding assistant using Rag and Sem-Rag Context-augmented AI coding assistant using Rag and Sem-Rag Jun 10, 2024 am 11:08 AM

Improve developer productivity, efficiency, and accuracy by incorporating retrieval-enhanced generation and semantic memory into AI coding assistants. Translated from EnhancingAICodingAssistantswithContextUsingRAGandSEM-RAG, author JanakiramMSV. While basic AI programming assistants are naturally helpful, they often fail to provide the most relevant and correct code suggestions because they rely on a general understanding of the software language and the most common patterns of writing software. The code generated by these coding assistants is suitable for solving the problems they are responsible for solving, but often does not conform to the coding standards, conventions and styles of the individual teams. This often results in suggestions that need to be modified or refined in order for the code to be accepted into the application

Seven Cool GenAI & LLM Technical Interview Questions Seven Cool GenAI & LLM Technical Interview Questions Jun 07, 2024 am 10:06 AM

To learn more about AIGC, please visit: 51CTOAI.x Community https://www.51cto.com/aigc/Translator|Jingyan Reviewer|Chonglou is different from the traditional question bank that can be seen everywhere on the Internet. These questions It requires thinking outside the box. Large Language Models (LLMs) are increasingly important in the fields of data science, generative artificial intelligence (GenAI), and artificial intelligence. These complex algorithms enhance human skills and drive efficiency and innovation in many industries, becoming the key for companies to remain competitive. LLM has a wide range of applications. It can be used in fields such as natural language processing, text generation, speech recognition and recommendation systems. By learning from large amounts of data, LLM is able to generate text

Can fine-tuning really allow LLM to learn new things: introducing new knowledge may make the model produce more hallucinations Can fine-tuning really allow LLM to learn new things: introducing new knowledge may make the model produce more hallucinations Jun 11, 2024 pm 03:57 PM

Large Language Models (LLMs) are trained on huge text databases, where they acquire large amounts of real-world knowledge. This knowledge is embedded into their parameters and can then be used when needed. The knowledge of these models is "reified" at the end of training. At the end of pre-training, the model actually stops learning. Align or fine-tune the model to learn how to leverage this knowledge and respond more naturally to user questions. But sometimes model knowledge is not enough, and although the model can access external content through RAG, it is considered beneficial to adapt the model to new domains through fine-tuning. This fine-tuning is performed using input from human annotators or other LLM creations, where the model encounters additional real-world knowledge and integrates it

To provide a new scientific and complex question answering benchmark and evaluation system for large models, UNSW, Argonne, University of Chicago and other institutions jointly launched the SciQAG framework To provide a new scientific and complex question answering benchmark and evaluation system for large models, UNSW, Argonne, University of Chicago and other institutions jointly launched the SciQAG framework Jul 25, 2024 am 06:42 AM

Editor |ScienceAI Question Answering (QA) data set plays a vital role in promoting natural language processing (NLP) research. High-quality QA data sets can not only be used to fine-tune models, but also effectively evaluate the capabilities of large language models (LLM), especially the ability to understand and reason about scientific knowledge. Although there are currently many scientific QA data sets covering medicine, chemistry, biology and other fields, these data sets still have some shortcomings. First, the data form is relatively simple, most of which are multiple-choice questions. They are easy to evaluate, but limit the model's answer selection range and cannot fully test the model's ability to answer scientific questions. In contrast, open-ended Q&A

SOTA performance, Xiamen multi-modal protein-ligand affinity prediction AI method, combines molecular surface information for the first time SOTA performance, Xiamen multi-modal protein-ligand affinity prediction AI method, combines molecular surface information for the first time Jul 17, 2024 pm 06:37 PM

Editor | KX In the field of drug research and development, accurately and effectively predicting the binding affinity of proteins and ligands is crucial for drug screening and optimization. However, current studies do not take into account the important role of molecular surface information in protein-ligand interactions. Based on this, researchers from Xiamen University proposed a novel multi-modal feature extraction (MFE) framework, which for the first time combines information on protein surface, 3D structure and sequence, and uses a cross-attention mechanism to compare different modalities. feature alignment. Experimental results demonstrate that this method achieves state-of-the-art performance in predicting protein-ligand binding affinities. Furthermore, ablation studies demonstrate the effectiveness and necessity of protein surface information and multimodal feature alignment within this framework. Related research begins with "S

Five schools of machine learning you don't know about Five schools of machine learning you don't know about Jun 05, 2024 pm 08:51 PM

Machine learning is an important branch of artificial intelligence that gives computers the ability to learn from data and improve their capabilities without being explicitly programmed. Machine learning has a wide range of applications in various fields, from image recognition and natural language processing to recommendation systems and fraud detection, and it is changing the way we live. There are many different methods and theories in the field of machine learning, among which the five most influential methods are called the "Five Schools of Machine Learning". The five major schools are the symbolic school, the connectionist school, the evolutionary school, the Bayesian school and the analogy school. 1. Symbolism, also known as symbolism, emphasizes the use of symbols for logical reasoning and expression of knowledge. This school of thought believes that learning is a process of reverse deduction, through existing

SK Hynix will display new AI-related products on August 6: 12-layer HBM3E, 321-high NAND, etc. SK Hynix will display new AI-related products on August 6: 12-layer HBM3E, 321-high NAND, etc. Aug 01, 2024 pm 09:40 PM

According to news from this site on August 1, SK Hynix released a blog post today (August 1), announcing that it will attend the Global Semiconductor Memory Summit FMS2024 to be held in Santa Clara, California, USA from August 6 to 8, showcasing many new technologies. generation product. Introduction to the Future Memory and Storage Summit (FutureMemoryandStorage), formerly the Flash Memory Summit (FlashMemorySummit) mainly for NAND suppliers, in the context of increasing attention to artificial intelligence technology, this year was renamed the Future Memory and Storage Summit (FutureMemoryandStorage) to invite DRAM and storage vendors and many more players. New product SK hynix launched last year

See all articles