"At the highest level of generality, a general CBR cycle may be described by the following four processes:
– Case-Based Reasoning: Foundational Issues, Methodological Variations, and System Approaches. Agnar Aamodt & Enric Plaza. AI Communications. IOS Press, Vol. 7: 1, pp. 39-59.
Abstract: Candidate generation is the first stage in recommendation systems, where a light-weight system is used to retrieve potentially relevant items for an input user. These candidate items are then ranked and pruned in later stages of recommender systems using a more complex ranking model. Since candidate generation is the top of the recommendation funnel, it is important to retrieve a high-recall candidate set to feed into downstream ranking models. A common approach for candidate generation is to leverage approximate nearest neighbor (ANN) search from a single dense query embedding; however, this approach this can yield a low-diversity result set with many near duplicates. As users often have multiple interests, candidate retrieval should ideally return a diverse set of candidates reflective of the user's multiple interests.
In the AI-driven era, customer service has evolved to be more efficient and self-learning. AI systems help companies in a variety of ways including improving customer satisfaction ratings, reducing operational costs, and increasing revenue. AI has many other advantages for customer service that human agents cannot compete with -- it is always available, 24/7 and never gets tired or distracted. One of the leading AI systems in this area is CBR Systems' machine learning help desk system. Case Based Reasoning (CBR) is an AI technique that is increasingly used by customer service departments to improve their performance and help desk software providers to offer even more intelligent solutions for their customers.
K-nearest neighbors (KNN) is a type of supervised learning machine learning algorithm and can be used for both regression and classification tasks. A supervised machine learning algorithm is dependent on labeled input data which the algorithm learns on and uses its learnt knowledge to produce accurate outputs when unlabeled data is inputted. The use of KNN is to make predictions on the test data set based on the characteristics (labeled data) of the training data. The method used to make these predictions is by calculating the distance between the test data and training data, assuming that similar characteristics or attributes of the data points exist within close proximity. It allows us to identify and assign the category of the new data whilst taking into consideration its characteristics based on learned data points from the training data.
Neural network embeddings are a low-dimensional representation of input data that give rise to a variety of applications. Embeddings have some interesting capabilities, as they are able to capture the semantics of the data points. This is especially useful for unstructured data like images and videos, so you can not only encode pixel similarities but also some more complex relationships. Performing searches over these embeddings gives rise to a lot of use cases like classification, building up the recommendation systems, or even anomaly detection. One of the primary benefits of performing a nearest neighbor search on embeddings to accomplish these tasks is that there is no need to create a custom network for every new problem, you can often just use pre-trained models.
Technological innovation is increasing at a rapid pace and has made digital storage extremely cheap and accessible. Additionally, most people now have phones with cameras that are able to capture high quality images. The majority of images taken are viewed a few times and then sent to sit on a hard drive or some cloud storage service. I am no different, and since I had some extra time during the COVID-19 lockdowns, I came up with some software to give the photos in people's libraries a second life. This software creates photo mosaics.
K Nearest Neighbors or KNN is a standard Machine Learning algorithm used for classification. In KNN, we plot already labeled points with their label and then define decision boundaries based on the value of the hyperparameter "K". Hyperparameter just means a parameter that we control and can use for tuning. "K" is used to represent how many of the nearest neighbors we should take into account when determining the class of a new point. In this post we'll cover how to do KNN on two datasets, one contrived sample dataset and one more realistic dataset about wine from sklearn.
The term Artificial Intelligence (AI) has recently become a hot topic, however, there are currently some misunderstandings about this term, for instance, it has been used as a synonym for Machine Learning (ML), however, ML is only a part of the whole AI. There are two main reasons to explain this, the first one is the fact that ML is the best known of all techniques, and the second one is because of the similarities between learning and "intelligent behaviour". Machine Learning is the ability to train a computer system to perform a task by giving as input either data or, alternatively, an equivalent source of information that allows it to automatically associate, segment and/or classify said data. In other words, the computer can learn something in a certain way and therefore, act with "intelligence". Moreover, ML is not a synonym for AI, because AI does not only focus on learning, AI has more factors to be able to operate autonomously in new and uncertain environments and adapt to them accordingly.
The unsupervised K- Nearest Neighbour (KNN) algorithm is perhaps the most straightforward machine learning algorithm. However, a simple algorithm does not mean that analyzing the results is equally simple. As per my research, there are not many documented approaches to analyzing the results of the KNN algorithm. In this article, I will show you how to analyze and understand the results of the unsupervised KNN algorithm. I will be using a dataset on cars.
It's simple math, really: In a family with eight children, it stands to reason, surely one of them must be queer. Bridgerton has defied other expectations of a Regency-era love story: It is set in an alternate universe where the upper class is fully integrated and race is not an issue. The show's first two seasons focus on interracial romances, and the second season at least obliquely references the history of British colonialism in India. There's one obvious candidate for such a storyline: On the show, Eloise is the most outspoken, most feminist Bridgerton sibling. She is not interested in becoming a debutante, delaying her appearance to pursue another year of studies. She often dismisses marriage, questioning why a husband and children are all that are waiting in store for women.