- Referenced in 3041 articles
- empirical analysis of machine learning algorithms. The archive was created as an ftp archive ... world as a primary source of machine learning data sets. As an indication...
- Referenced in 456 articles
- problems. We show how the resulting learning algorithm can be applied to a variety ... about the performance of the weak learning algorithm. We also study generalizations ... boosting algorithm to the problem of learning functions whose range, rather than being binary...
- Referenced in 342 articles
- state-of-the-art machine learning algorithms for medium-scale supervised and unsupervised problems. This...
- Referenced in 255 articles
- introduced by the publications ”A fast learning algorithm for deep belief nets” (G. E. Hinton...
- Referenced in 384 articles
- graphical lasso  is an algorithm for learning the structure in an undirected Gaussian graphical ... explain this behavior, and propose new algorithms that appear to outperform glasso. By studying...
- Referenced in 260 articles
- problem of learning a ranking function. The optimization algorithms used in SVMlight are described ... this version is an algorithm for learning ranking functions [Joachims, 2002c]. The goal ... learn a function from preference examples, so that it orders a new set of objects ... recommender systems. Futhermore, this version includes an algorithm for training large-scale transductive SVMs...
- Referenced in 99 articles
- inter-pattern distance-based constructive learning algorithm Multi-layer networks of threshold logic units offer ... systems. A new constructive neural network learning algorithm (DistAl) based on inter-pattern distance ... significant advantage over other constructive learning algorithms that use an iterative (and often time consuming ... that DistAl compares favorably with other learning algorithms for pattern classification...
- Referenced in 102 articles
- unsupervised learning, etc. It includes evolutionary learning algorithms based on different approaches: Pittsburgh, Michigan ... well as the integration of evolutionary learning techniques with different pre-processing techniques, allowing...
- Referenced in 93 articles
- open source computer vision and machine learning software library. OpenCV was built to provide ... library has more than 2500 optimized algorithms, which includes a comprehensive set of both classic ... computer vision and machine learning algorithms. These algorithms can be used to detect and recognize...
- Referenced in 67 articles
- Margin Nearest Neighbor (LMNN), a metric learning algorithm first introduced by Kilian Q. Weinberger, John ... LMNN is a metric learning algorithm to improve k-nearest neighbor classification by learning...
- Referenced in 62 articles
- efficient and general multiple kernel learning algorithm, based on semi-infinite linear programming, has been ... However, it turns out that this iterative algorithm needs numerous iterations for converging towards ... that encourages sparse kernel combinations. Apart from learning the combination, we solve a standard ... combination of multiple kernels. We propose an algorithm, named SimpleMKL, for solving this MKL problem...
- Referenced in 58 articles
- this paper, a multi-label lazy learning approach named ML-KNN is presented, which ... from the traditional K-nearest neighbor (KNN) algorithm. In detail, for each unseen instance ... three different real-world multi-label learning problems, i.e. Yeast gene functional analysis, natural scene ... some well-established multi-label learning algorithms...
- Referenced in 59 articles
- both local search and tree search algorithms by orders of magnitude on certain instance distributions ... hyperparameter optimization of machine learning algorithms, scaling better to high dimensions and discrete input dimensions...
- Referenced in 56 articles
- hybrid (MMHC and RSMAX2) structure learning algorithms for both discrete and Gaussian networks, along with...
- Referenced in 52 articles
- flexible and portable. It implements machine learning algorithms under the Gradient Boosting framework. XGBoost provides...
- Referenced in 72 articles
- text categorization. This work focuses on algorithms which learn from examples to perform multiclass text ... improved family of boosting algorithms. We describe in detail an implementation, called BoosTexter...
- Referenced in 32 articles
- very simple model-based reinforcement learning algorithm which can attain near-optimal average reward ... than Brafman and Tennenholtz’s LSG algorithm for learning in single controller stochastic games ... generalizes the algorithm by Monderer and Tennenholtz for learning in repeated ... games. (6) It is the only algorithm for learning in repeated games, to date, which...
- Referenced in 97 articles
- ADAGRAD: adaptive gradient algorithm; Adaptive subgradient methods for online learning and stochastic optimization. We present ... online learning which employ proximal functions to control the gradient steps of the algorithm ... proximal function, which significantly simplifies setting a learning rate and results in regret guarantees that ... chosen in hindsight. We give several efficient algorithms for empirical risk minimization problems with common...
- Referenced in 46 articles
- Word Representation. GloVe is an unsupervised learning algorithm for obtaining vector representations for words. Training...
- Referenced in 66 articles
- AntNet, a novel approach to the adaptive learning of routing tables in communications networks. AntNet ... called stigmergy. We compare our algorithm with six state-of-the-art routing algorithms coming ... from the telecommunications and machine learning fields. The algorithms’ performance is evaluated over...