Kernel Trick - Revision history

BPeat at 20:38, 28 April 2024

2024-04-28T20:38:42Z

2024-03-06T02:58:54Z

2023-09-13T15:47:45Z

2023-08-17T00:12:36Z

2023-07-11T06:15:51Z

2023-07-11T06:14:40Z

2023-06-30T18:25:46Z

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@@ Line 25: / Line 25: @@
 ** [[Isomap]]
 ** [[Local Linear Embedding (LLE)]]
-* [[In-Context Learning (ICL)]] ... [[Context]] ... [[Causation vs. Correlation]] ... [[Autocorrelation]] ... [[Out-of-Distribution (OOD) Generalization]] ... [[Transfer Learning]]
+* [[Perspective]] ... [[Context]] ... [[In-Context Learning (ICL)]] ... [[Transfer Learning]] ... [[Out-of-Distribution (OOD) Generalization]]
 * [https://towardsdatascience.com/the-kernel-trick-c98cdbcaeb3f The Kernel Trick in Support Vector Classification | Drew Wilimitis]
 * [https://prateekvjoshi.com/2012/09/01/kernel-functions-for-machine-learning/ Kernel Functions For Machine Learning | Prateek Joshi]

@@ Line 18: / Line 18: @@
 * [[Math for Intelligence]] ... [[Finding Paul Revere]] ... [[Social Network Analysis (SNA)]] ... [[Dot Product]] ... [[Kernel Trick]]
-* [[AI Solver]] ... [[Algorithms]] ... [[Algorithm Administration|Administration]] ... [[Model Search]] ... [[Discriminative vs. Generative]] ... [[Optimizer]] ... [[Train, Validate, and Test]]
+* [[AI Solver]] ... [[Algorithms]] ... [[Algorithm Administration|Administration]] ... [[Model Search]] ... [[Discriminative vs. Generative]] ... [[Train, Validate, and Test]]
 * [[Embedding]] ... [[Fine-tuning]] ... [[Retrieval-Augmented Generation (RAG)|RAG]] ... [[Agents#AI-Powered Search|Search]] ... [[Clustering]] ... [[Recommendation]] ... [[Anomaly Detection]] ... [[Classification]] ... [[Dimensional Reduction]].  [[...find outliers]]
 * [[Dimensional Reduction]] Algorithms

@@ Line 19: / Line 19: @@
 * [[Math for Intelligence]] ... [[Finding Paul Revere]] ... [[Social Network Analysis (SNA)]] ... [[Dot Product]] ... [[Kernel Trick]]
 * [[AI Solver]] ... [[Algorithms]] ... [[Algorithm Administration|Administration]] ... [[Model Search]] ... [[Discriminative vs. Generative]] ... [[Optimizer]] ... [[Train, Validate, and Test]]
-* [[Embedding]] ... [[Fine-tuning]] ... [[Agents#AI-Powered Search|Search]] ... [[Clustering]] ... [[Recommendation]] ... [[Anomaly Detection]] ... [[Classification]] ... [[Dimensional Reduction]].  [[...find outliers]]
+* [[Embedding]] ... [[Fine-tuning]] ... [[Retrieval-Augmented Generation (RAG)|RAG]] ... [[Agents#AI-Powered Search|Search]] ... [[Clustering]] ... [[Recommendation]] ... [[Anomaly Detection]] ... [[Classification]] ... [[Dimensional Reduction]].  [[...find outliers]]
 * [[Dimensional Reduction]] Algorithms
 * [[Backpropagation]] ... [[Feed Forward Neural Network (FF or FFNN)|FFNN]] ... [[Forward-Forward]] ... [[Activation Functions]] ...[[Softmax]] ... [[Loss]] ... [[Boosting]] ... [[Gradient Descent Optimization & Challenges|Gradient Descent]] ... [[Algorithm Administration#Hyperparameter|Hyperparameter]] ... [[Manifold Hypothesis]] ... [[Principal Component Analysis (PCA)|PCA]]

@@ Line 19: / Line 19: @@
 * [[Math for Intelligence]] ... [[Finding Paul Revere]] ... [[Social Network Analysis (SNA)]] ... [[Dot Product]] ... [[Kernel Trick]]
 * [[AI Solver]] ... [[Algorithms]] ... [[Algorithm Administration|Administration]] ... [[Model Search]] ... [[Discriminative vs. Generative]] ... [[Optimizer]] ... [[Train, Validate, and Test]]
-* [[Embedding]]:  [[Agents#AI-Powered Search|Search]] ... [[Clustering]] ... [[Recommendation]] ... [[Anomaly Detection]] ... [[Classification]] ... [[Dimensional Reduction]] ... [[...find outliers]]
+* [[Embedding]] ... [[Fine-tuning]] ... [[Agents#AI-Powered Search|Search]] ... [[Clustering]] ... [[Recommendation]] ... [[Anomaly Detection]] ... [[Classification]] ... [[Dimensional Reduction]].  [[...find outliers]]
 * [[Dimensional Reduction]] Algorithms
 * [[Backpropagation]] ... [[Feed Forward Neural Network (FF or FFNN)|FFNN]] ... [[Forward-Forward]] ... [[Activation Functions]] ...[[Softmax]] ... [[Loss]] ... [[Boosting]] ... [[Gradient Descent Optimization & Challenges|Gradient Descent]] ... [[Algorithm Administration#Hyperparameter|Hyperparameter]] ... [[Manifold Hypothesis]] ... [[Principal Component Analysis (PCA)|PCA]]

@@ Line 25: / Line 25: @@
 ** [[Isomap]]
 ** [[Local Linear Embedding (LLE)]]
 * [https://towardsdatascience.com/the-kernel-trick-c98cdbcaeb3f The Kernel Trick in Support Vector Classification | Drew Wilimitis]
 * [https://prateekvjoshi.com/2012/09/01/kernel-functions-for-machine-learning/ Kernel Functions For Machine Learning | Prateek Joshi]
 * [https://en.wikipedia.org/wiki/Kernel_method Kernel method | Wikipedia]
 * [https://staff.ustc.edu.cn/~cheneh/paper_pdf/2017/Chu-Guan-Neurocomputing.pdf Efficient karaoke song recommendation via multiple kernel learning approximation | C. Guana, Y. Fub, X. Luc, E. Chena, X. Li, and H. Xiong]
 The word "kernel" is used in mathematics to denote a weighting function for a weighted sum or integral.
@@ Line 37: / Line 35: @@
 * induces a linear representation of the data in a vector space - no matter where the original data comes from | Scholkopf 1997
 * encodes the function class used for learning - solutions of kernel algorithms can be expressed as kernel expansions | Kimeldorf, Wabba 1971, Scholkopf, Herbrich, and Smola 2001
 The 'Kernel Trick' avoids the explicit mapping that is needed to get linear learning algorithms to learn a nonlinear function or decision boundary.

@@ Line 17: / Line 17: @@
 [https://www.google.com/search?q=Kernel+trick+Approximation+machine+learning+ML ...Google search]
-* [[Math for Intelligence]] ... [[Finding Paul Revere]] ... [[Social Network Analysis (SNA)]] ... [[Dot Product]] ... [[Kernel Trick]] ... [[Causation vs. Correlation]]
+* [[Math for Intelligence]] ... [[Finding Paul Revere]] ... [[Social Network Analysis (SNA)]] ... [[Dot Product]] ... [[Kernel Trick]]
 * [[AI Solver]]
 * [[...find outliers]]

@@ Line 2: / Line 2: @@
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-|keywords=artificial, intelligence, machine, learning, models, algorithms, data, singularity, moonshot, Tensorflow, Google, Nvidia, Microsoft, Azure, Amazon, AWS
+|keywords=ChatGPT, artificial, intelligence, machine, learning, GPT-4, GPT-5, NLP, NLG, NLC, NLU, models, data, singularity, moonshot, Sentience, AGI, Emergence, Moonshot, Explainable, TensorFlow, Google, Nvidia, Microsoft, Azure, Amazon, AWS, Hugging Face, OpenAI, Tensorflow, OpenAI, Google, Nvidia, Microsoft, Azure, Amazon, AWS, Meta, LLM, metaverse, assistants, agents, digital twin, IoT, Transhumanism, Immersive Reality, Generative AI, Conversational AI, Perplexity, Bing, You, Bard, Ernie, prompt Engineering LangChain, Video/Image, Vision, End-to-End Speech, Synthesize Speech, Speech Recognition, Stanford, MIT |description=Helpful resources for your journey with artificial intelligence; videos, articles, techniques, courses, profiles, and tools
 |description=Helpful resources for your journey with artificial intelligence; videos, articles, techniques, courses, profiles, and tools
 }}
 [https://www.youtube.com/results?search_query=Kernel+trick+Approximation YouTube search...]
 [https://www.google.com/search?q=Kernel+trick+Approximation+machine+learning+ML ...Google search]
 * [[AI Solver]]
 * [[...find outliers]]
 * [[Anomaly Detection]]
 * [[Dimensional Reduction]] Algorithms