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/semi-supervised-learning
点击访问原文链接

What Is Semi-Supervised 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 inspectionDave Bergmannmachine learningsupervisedunsupervised learningartificial intelligence (AI) modelsgradient descentcomputer visionobject detectionimage segmentationIBM Privacy Statementoverfitself-supervised learningclusteringself-supervised learningautoencodersconvolutionsEuclidian distancesemantic segmentationWatch all episodes of Mixture of Expertsself-supervised learningautoencodersupport vector machine (SVMs)SpotifyApple PodcastsFind more episodesDave BergmannEbook Data science and MLOps for data leaders Join forces with other leaders to drive the three essential pillars of MLOps and trustworthy AI: trust in data, trust in models and trust in processes. Read the ebookTraining Level up your ML expertise Learn fundamental concepts and build your skills with hands-on labs, courses, guided projects, trials and more. Explore ML coursesEbook Unlock the power of generative AI + ML Learn how to confidently incorporate generative AI and machine learning into your business. Read the ebookTechsplainers Podcast Machine learning explained Techsplainers by IBM breaks down the essentials of machine learning, from key concepts to real‑world use cases. Clear, quick episodes help you learn the fundamentals fast. Listen nowGuide Put AI to work: Driving ROI with gen AI Want to get a better return on your AI investments? Learn how scaling gen AI in key areas drives change by helping your best minds build and deliver innovative new solutions. Read the guideEbook How to choose the right foundation model Learn how to select the most suitable AI foundation model for your use case. Read the ebookAI 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 GraniteGuide How to thrive in this new era of AI with trust and confidence Dive into the 3 critical elements of a strong AI strategy: creating a competitive edge, scaling AI across the business and advancing trustworthy AI. Read the guideExplore watsonx OrchestrateExplore AI development toolsExplore AI servicesExplore watsonx OrchestrateExplore watsonx.ai“Realistic Evaluation of Deep Semi-Supervised Learning Algorithms”“A survey on semi-supervised learning”Transductive active learning – A new semi-supervised learning approach based on iteratively refined generative models to capture structure in data”“Semantic Segmentation with Active Semi-Supervised Learning”“Semi-supervised learning by Entropy Minimization”“Density-based semi-supervised clustering”“Semi-Supervised Learning with Ladder Networks”“Learning with Pseudo-Ensembles”“Temporal Ensembling for Semi-Supervised Learning”“Improved Techniques for Training GANs”"Realistic Evaluation of Deep Semi-Supervised Learning Algorithms""A survey on semi-supervised learning"Transductive active learning – A new semi-supervised learning approach based on iteratively refined generative models to capture structure in data"Semantic Segmentation with Active Semi-Supervised Learning""Semi-supervised learning by Entropy Minimization""Density-based semi-supervised clustering""Semi-Supervised Learning with Ladder Networks""Learning with Pseudo-Ensembles""Temporal Ensembling for Semi-Supervised Learning""Improved Techniques for Training GANs"

智能索引记录