Parallelized FPA-SVM: Parallelized Parameter Selection and Classification using Flower Pollination Algorithm and Support Vector Machine

Jean-Charles Coetsier [PDF Full Text] Support Vector Machines (SVM) is one of the most popular machine learning algorithm to perform classification tasks and help organizations in different ways to improve their efficiency. A lot of studies have been made to improve SVM including speed and accuracy. The algorithm possesses parameters that need precision tuning to

Parallelized FPA-SVM: Parallelized Parameter Selection and Classification using Flower Pollination Algorithm and Support Vector Machine

Jean-Charles Coetsier [PDF Full Text] Support Vector Machines (SVM) is one of the most popular machine learning algorithm to perform classification tasks and help organizations in different ways to improve their efficiency. A lot of studies have been made to improve SVM including speed and accuracy. The algorithm possesses parameters that need precision tuning to

Alternating Least Squares with Incremental Learning Bias

Than Htike Aung [PDF Full Text] Recommender systems provide personalized suggestions for every individual user in the system. Many recommender systems use collaborative filtering approach in which the system collects and analyzes users’ past behaviors, activities or preferences to produce high quality recommendations for the users. Among various collaborative recommendation techniques, model-based approaches are more

Alternating Least Squares with Incremental Learning Bias

Than Htike Aung [PDF Full Text] Recommender systems provide personalized suggestions for every individual user in the system. Many recommender systems use collaborative filtering approach in which the system collects and analyzes users’ past behaviors, activities or preferences to produce high quality recommendations for the users. Among various collaborative recommendation techniques, model-based approaches are more

Dimension Independent Cosine Similarity for Collaborative Filtering using MapReduce

Fei Shen [PDF Full Text] DIMSUM, an efficient and accurate all-pair similarity algorithm for real-world large scale dataset, tackles shuffle size problem of several similarity measures using MapReduce. The algorithm uses a sampling technique to reduce ‘power items’ and preserves similarities. This work presents an improved algorithm DIMSUM+ with a complex sampling technique to enhance DIMSUM so that

Dimension Independent Cosine Similarity for Collaborative Filtering using MapReduce

Fei Shen [PDF Full Text] DIMSUM, an efficient and accurate all-pair similarity algorithm for real-world large scale dataset, tackles shuffle size problem of several similarity measures using MapReduce. The algorithm uses a sampling technique to reduce ‘power items’ and preserves similarities. This work presents an improved algorithm DIMSUM+ with a complex sampling technique to enhance DIMSUM so that