温馨提示：本站仅提供公开网络链接索引服务，不存储、不篡改任何第三方内容，所有内容版权归原作者所有
AI智能索引来源：http://www.ibm.com/think/topics/classification-machine-learning
点击访问原文链接

What is Classification in Machine Learning? | IBM

进入原网站 导航首页
WelcomeOverviewMachine learning typesMachine learning algorithmsStatistical machine learningLinear algebra for machine learningUncertainty quantificationBias variance tradeoffBayesian StatisticsSingular value decompositionOverviewFeature selectionFeature extractionVector embeddingLatent spacePrincipal component analysisLinear discriminant analysisUpsamplingDownsamplingSynthetic dataData leakageOverviewLinear regressionLasso regressionRidge regressionState space modelTime seriesAutoregressive modelOverviewDecision treesK-nearest neighbors (KNNs)Naive bayesRandom forestSupport vector machineLogistic regressionOverviewBoostingBaggingGradient boostingGradient boosting classifierOverviewTransfer learningOverviewOverviewK means clusteringHierarchical clusteringA priori algorithmGaussian mixture modelAnomaly detectionOverviewCollaborative filteringContent based filteringOverviewReinforcement learning human feedbackOverviewOverviewBackpropagationEncoder-decoder modelRecurrent neural networksLong short-term memory (LSTM)Convolutional neural networksOverviewAttention mechanismGrouped query attentionPositional encodingAutoencoderMamba modelGraph neural networkOverviewGenerative modelGenerative AI vs. predictive AIOverviewReasoning modelsSmall language modelsInstruction tuningLLM parametersLLM temperatureLLM benchmarksLLM customizationDiffusion modelsVariational autoencoder (VAE)Generative adversarial networks (GANs)OverviewVision language modelsTutorial: Build an AI stylistTutorial: Multimodal AI queries using LlamaTutorial: Multimodal AI queries using PixtralTutorial: Automatic podcast transcription with GraniteTutorial: PPT AI image analysis answering systemOverviewGraphRAGTutorial: Build a multimodal RAG system with Docling and GraniteTutorial: Evaluate RAG pipline using RagasTutorial: RAG chunking strategiesTutorial: Graph RAG using knowledge graphsTutorial: Inference scaling to improve multimodal RAGOverviewVibe codingVisit the 2025 Guide to AI AgentsLLM trainingOverviewLoss functionTraining dataModel parametersGradient descentStochastic gradient descentHyperparameter tuningLearning rateOverviewParameter efficient fine tuning (PEFT)LoRATutorial: Fine tuning Granite model with LoRARegularizationFoundation modelsOverfittingUnderfittingFew shot learningZero shot learningKnowledge distillationMeta learningData augmentationCatastrophic forgettingOverviewScikit-learnXGboostPyTorchOverviewAI lifecyleAI inferenceModel deploymentMachine learning pipelineData labelingModel risk managementModel driftAutoMLModel selectionFederated learningDistributed machine learningAI stackOverviewNatural language understandingOverviewSentiment analysisTutorial: Spam text classifier with PyTorchMachine translationOverviewInformation retrievalInformation extractionTopic modelingLatent semantic analysisLatent Dirichlet AllocationNamed entity recognitionWord embeddingsBag of wordsIntelligent searchSpeech recognitionStemming and lemmatizationText summarizationConversational AIConversational analyticsNatural language generationOverviewImage classificationObject detectionInstance segmentationSemantic segmentationOptical character recognitionImage recognitionVisual inspectionIvan Belcicmachine learningAI modelsneural networksIBM Privacy StatementWatch all episodes of Mixture of Expertsmachine learning algorithmArtificial intelligence (AI)data sciencesupervised machine learninglabeled dataGradient descentfine-tunedUnsupervised learningunsupervised learning and supervised learningsemisupervised learningoverfittingnatural language processingsentiment analysisfraud detectiondeep learningLogistic regressionLogistic regressionlinear regressiondecision treesrandom forestSupport vector machine (SVM)K-nearest neighbors (KNN)Dimensionality reductionNaive BayesNatural language processingsentiment analysisbag of wordsboostingPositional encodingconfusion matrixReport IBM X-Force Threat Intelligence Index 2026 Gain insights to prepare and respond to cyberattacks with greater speed and effectiveness with the IBM X-Force® Threat Intelligence Index. Read the reportReport IBM is named a Leader in Data Science & Machine Learning Learn why IBM has been recognized as a Leader in the 2025 Gartner® Magic Quadrant™ for Data Science and Machine Learning Platforms. Read the reportAI models Explore IBM Granite IBM Granite is our family of open, performant and trusted AI models, tailored for business and optimized to scale your AI applications. Explore language, code, time series and guardrail options. Meet GraniteReport AI in Action 2024 We surveyed 2,000 organizations about their AI initiatives to discover what's working, what's not and how you can get ahead. Read the reportExplainer Supervised learning models Explore supervised learning approaches such as support vector machines and probabilistic classifiers. Read the explainerTraining Hands-on with generative AI Learn fundamental concepts and build your skills with hands-on labs, courses, guided projects, trials and more. Learn generative AIEbook How to choose the right foundation model Learn how to select the most suitable AI foundation model for your use case. Read the ebookExplore watsonx OrchestrateExplore AI solutionsExplore AI servicesExplore watsonx OrchestrateExplore watsonx.aihttps://www.sciencedirect.com/science/article/abs/pii/S0002914915023590https://www.jmlr.org/papers/v25/23-0286.html

智能索引记录