Meta-Domain Adaptation
Definition Meta-Domain Adaptation in AI marketing refers to a strategy that employs machine learning to understand and adapt to different but related domains. It works by generalizing the learned knowledge from several base domains to assist in the adaptation to a novel target domain. This way, it enables marketers to improve targeting and personalization strategies […]
Model Distillation
Definition Model Distillation in AI marketing is a process used to create compact, efficient models that replicate the behavior of larger, more complex ones. This sophisticated technique involves training a second model (the student) to imitate the predictions of the original model (the teacher). The resulting distilled model is more streamlined and efficient, making real-time […]
Model Stacking
Definition Model Stacking in AI refers to the process of using multiple machine learning models to improve predictive accuracy. Instead of relying on one model, various models are trained and their predictions are combined, typically using a method like voting or averaging. This offers a more reliable and efficient outcome, reducing the chance of misjudgment […]
Manifold Mixup
Definition Manifold Mixup is a method used in AI for improving the performance and robustness of neural networks. It operates by encouraging the model to make consistent predictions across different mixtures of the same data. By mixing both inputs and hidden state representations during training, the model learns successful generalizations and can predict better outcomes. […]
Model Compression
Definition Model Compression in AI marketing refers to the process of reducing the size of a machine learning model without significantly compromising its output accuracy. This technique enables faster computation and requires less storage space, making the model more efficient and accessible. It is particularly useful in edge devices or systems with limited computational resources. […]
Model Reuse
Definition In marketing AI, Model Reuse refers to the practice of utilizing previously developed and trained AI models in new, similar contexts or applications. Its main aim is to optimize resources, decrease development time and increase efficiency by leveraging existing solutions. This avoids the need to train a new model from scratch, saving time and […]
Mean Shift Clustering
Definition Mean Shift Clustering is a type of unsupervised machine learning algorithm utilized in artificial intelligence. It involves identifying and analyzing data clusters based on the density of data points in that region. The method shifts the data points towards the densest part of the cluster, intending to converge towards the most likely cluster centroid. […]
Monte Carlo Tree Search (MCTS)
Definition Monte Carlo Tree Search (MCTS) is an AI search algorithm used in marketing for decision-making tasks by simulating possible outcomes to determine the optimal move. MCTS uses random sampling combined with tree-based planning to explore different scenarios and evaluate their potential effectiveness. It’s frequently used in situations with a large number of possible outcomes, […]
Markov Chain Monte Carlo (MCMC)
Definition In the context of AI and marketing, the Markov Chain Monte Carlo (MCMC) is a class of algorithms for sampling from a probability distribution. These algorithms are used to generate a sequence of samples from the multivariate distribution of many variables, such as customer behavior, conversion rates, or sales forecasts. Thus, MCMC helps marketers […]
Monte Carlo Methods
Definition Monte Carlo methods in marketing AI refer to a statistical analysis approach that uses randomness to solve problems. They simulate multiple possible outcomes by assigning probabilities to each one. This method is particularly useful in predicting various scenarios, making strategies more robust and adaptable to uncertainties. Key takeaway Monte Carlo Methods in AI marketing […]
