in machine learning, boosting is an ensemble meta-algorithm for primarily reducing bias, and also variance in supervised learning, and a family of machine learning algorithms that convert weak learners to strong ones. boosting is based on the question posed by kearns and valiant 1988, 1989 : 'can a set of weak learners create a single strong learner?' a weak learner is defined to be a .
learning classifier systems, or lcs, are a paradigm of rule-based machine learning methods that combine a discovery component with a learning component. learning classifier systems seek to identify a set of context-dependent rules that collectively store and apply knowledge in a piecewise manner in order to make predictions. this approach allows complex solution spaces to be broken up into smaller, simpler parts. the founding concepts behind learning classifier systems came from attempts to mode
a gaussian process can be used as a prior probability distribution over functions in bayesian inference. given any set of n points in the desired domain of your functions, take a multivariate gaussian whose covariance matrix parameter is the gram matrix of your n points with some desired kernel, and sample from that gaussian. for solution of the multi-output prediction problem, gaussian .
cascading classifiers are trained with several hundred 'positive' sample views of a particular object and arbitrary 'negative' images of the same size. after the classifier is trained it can be applied to a region of an image and detect the object in question. to search for the object in the entire frame, the search window can be moved across .
the bayes optimal classifier is a classification technique. it is an ensemble of all the hypotheses in the hypothesis space. on average, no other ensemble can outperform it. the naive bayes optimal classifier is a version of this that assumes that the data is conditionally independent on the class and makes the computation more feasible.
meta learning is a subfield of machine learning where automatic learning algorithms are applied on metadata about machine learning experiments. as of 2017 the term had not found a standard interpretation, however the main goal is to use such metadata to understand how automatic learning can become flexible in solving learning problems, hence to improve the performance of existing learning .
still, a comprehensive comparison with other classification algorithms in 2006 showed that bayes classification is outperformed by other approaches, such as boosted trees or random forests. an advantage of naive bayes is that it only requires a small number of training data to estimate the parameters necessary for classification. citation needed
the above method will provide efficient computation for the relative small number of classification. support vector machine. another continuous but not differentiable alternative to the 0/1-loss is the ’hinge-loss’, which can be defined as the following equation
the naive bayes classifier combines this model with a decision rule. one common rule is to pick the hypothesis that is most probable; this is known as the maximum a posteriori or map decision rule. the corresponding classifier, a bayes classifier, is the function that assigns a class label ^ = for some k as follows:
a multilayer perceptron mlp is a class of feedforward artificial neural network ann . the term mlp is used ambiguously, sometimes loosely to refer to any feedforward ann, sometimes strictly to refer to networks composed of multiple layers of perceptrons with threshold activation ; see § terminology.multilayer perceptrons are sometimes colloquially referred to as 'vanilla' neural networks .
supervised learning is the machine learning task of learning a function that maps an input to an output based on example input-output pairs. it infers a function from labeled training data consisting of a set of training examples. in supervised learning, each example is a pair consisting of an input object typically a vector and a desired output value also called the supervisory signal .
motivation. the bias-variance tradeoff is a central problem in supervised learning. ideally, one wants to choose a model that both accurately captures the regularities in its training data, but also generalizes well to unseen data. unfortunately, it is typically impossible to do both simultaneously.
in the tables, the first and second columns contain the chinese character representing the classifier, in traditional and simplified versions when they differ. the third column gives the pronunciation in standard mandarin chinese, using pinyin; the fourth gives the cantonese pronunciation, using yale romanization; and the fifth the minnan pronunciation taiwan .
svm based one-class classification occ relies on identifying the smallest hypersphere with radius r, and center c consisting of all the data points. this method is called support vector data description svdd .
an industrial flexible manufacturing system fms consists of robots, computer-controlled machines, computer numerical controlled machines , instrumentation devices, computers, sensors, and other stand alone systems such as inspection machines. the use of robots in the production segment of manufacturing industries promises a variety of benefits ranging from high utilization to high volume .
vowpal wabbit also known as 'vw' is an open-source fast online interactive machine learning system library and program developed originally at yahoo research, and currently at microsoft research.it was started and is led by john langford.vowpal wabbit's interactive learning support is particularly notable including contextual bandits, active learning, and forms of guided reinforcement learning.
multi-task learning mtl is a subfield of machine learning in which multiple learning tasks are solved at the same time, while exploiting commonalities and differences across tasks. this can result in improved learning efficiency and prediction accuracy for the task-specific models, when compared to training the models separately.