Learning library
656 articles publish hue
Simulated Annealing Algorithm Artificial Intelligence ka ek probabilistic Local Search Algorithm hai jo temperature aur acceptance probability ka use karke optimization problems ko solve karta hai. Is article mein Simulated Annealing ki working, formula, algorithm, examples, advantages, disadvantages, applications, interview questions aur MCQs ko easy Hinglish mein detail se explain kiya gaya hai.
Hyperplane SVM ka decision boundary hai jo different classes ko separate karta hai. Is article me Hyperplane, Decision Surface, Margin, Support Vectors aur unke working steps explain kiye gaye hain with real‑life examples.
Hill Climbing Algorithm Artificial Intelligence ka ek important Local Search Algorithm hai jo current solution ko continuously improve karke better neighboring state ki taraf move karta hai. Is article mein Hill Climbing ki working, types, algorithm, pseudocode, advantages, disadvantages, applications, interview questions aur MCQs ko easy Hinglish mein detail se explain kiya gaya hai.
Local Search Algorithms in Artificial Intelligence are optimization techniques that improve the current solution by exploring neighboring states instead of searching the entire state space. This article explains the concept, need, characteristics, working, types, advantages, disadvantages, applications, interview questions, and MCQs in easy Hinglish.
SVM me Kernel Function data ko higher‑dimensional space me transform karta hai taaki non‑linear data ko easily separate kiya ja sake. Is article me Linear, Polynomial aur Gaussian (RBF) kernels ke definition, formula, applications aur comparison explain kiye gaye hain.
A Search Algorithm* Artificial Intelligence ka ek powerful informed search algorithm hai jo g(n) aur h(n) ko combine karke optimal path find karta hai. Is article mein A* Search ki working, evaluation function, algorithm, pseudocode, examples, advantages, disadvantages, applications, interview questions aur MCQs ko easy Hinglish mein detail se explain kiya gaya hai.
Expectation Maximization (EM) ek iterative unsupervised ML algorithm hai jo hidden ya missing data ke saath model parameters estimate karta hai. Is article me steps, working, examples, advantages aur limitations explain kiye gaye hain.
Bayesian Belief Network (BBN) ek probabilistic graphical model hai jo variables aur unke conditional dependencies ko Directed Acyclic Graph (DAG) ke form me represent karta hai. Is article me structure, working, examples, applications, advantages aur limitations explain kiye gaye hain.
Bayes Optimal Classifier ek theoretical classifier hai jo sabhi hypotheses consider karta hai, jabki Naive Bayes ek practical algorithm hai jo feature independence assume karta hai. Is article me dono ke working, examples, advantages, limitations aur differences explain kiye gaye hain.
Concept Learning ek supervised ML approach hai jisme machine labeled training examples se general rules seekh kar naye data ko classify karti hai. Is article me terminologies, process, version space, candidate elimination algorithm, applications, advantages aur limitations explain kiye gaye hain.
Bayes’ Theorem ek mathematical probability concept hai jo Machine Learning me prior knowledge aur new evidence ko combine karke updated probability calculate karta hai. Is article me formula, components, examples, applications, advantages aur limitations explain kiye gaye hain.
ayesian Learning ek statistical ML approach hai jo Bayes’ Theorem par based hai. Is article me prior, likelihood, posterior, evidence, Naive Bayes classifier, types, applications, advantages aur limitations explain kiye gaye hain.
