Inductive learning, or induction, is the process of creating generalizations from individual instances.
Comprehensive and accurate models of the surrounding environment are crucial for solving the challenges of autonomous operation. However, only a limited amount of information is perceived through the sensors which are limited regarding their capabilities, the field of view and the kind of data they provide. While sensors like LIDAR, Radar, Kinect provide 3D data including all spatial dimensions, cameras on the other hand only provide a 2D view of the surrounding. In the past, many attempts have been made to actually extract the 3D data out of 2D images coming from the camera. The human visual system is remarkably successful in solving this task, while algorithms very often fail to reconstruct and infer a depth map out of an image.
In recent years, state-of-the-art methods for supervised learning have exploited increasingly gradient boosting techniques, with mainstream efficient implementations such as xgboost or lightgbm. One of the key points in generating proficient methods is Feature Selection (FS). It consists in selecting the right valuable effective features. When facing hundreds of these features, it becomes critical to select best features. While filter and wrappers methods have come to some maturity, embedded methods are truly necessary to find the best features set as they are hybrid methods combining features filtering and wrapping. In this work, we tackle the problem of finding through machine learning best a priori trades from an algorithmic strategy. We derive this new method using coordinate ascent optimization and using block variables. We compare our method to Recursive Feature Elimination (RFE) and Binary Coordinate Ascent (BCA). We show on a real life example the capacity of this method to select good trades a priori. Not only this method outperforms the initial trading strategy as it avoids taking loosing trades, it also surpasses other method, having the smallest feature set and the highest score at the same time. The interest of this method goes beyond this simple trade classification problem as it is a very general method to determine the optimal feature set using some information about features relationship as well as using coordinate ascent optimization.
Semi-supervised learning methods are motivated by the relative paucity of labeled data and aim to utilize large sources of unlabeled data to improve predictive tasks. It has been noted, however, such improvements are not guaranteed in general in some cases the unlabeled data impairs the performance. A fundamental source of error comes from restrictive assumptions about the unlabeled features. In this paper, we develop a semi-supervised learning approach that relaxes such assumptions and is robust with respect to labels missing at random. The approach ensures that uncertainty about the classes is propagated to the unlabeled features in a robust manner. It is applicable using any generative model with associated learning algorithm. We illustrate the approach using both standard synthetic data examples and the MNIST data with unlabeled adversarial examples.
Most network-based machine learning methods assume that the labels of two adjacent samples in the network are likely to be the same. However, assuming the pairwise relationship between samples is not complete. The information a group of samples that shows very similar pattern and tends to have similar labels is missed. The natural way overcoming the information loss of the above assumption is to represent the feature dataset of samples as the hypergraph. Thus, in this paper, we will present the un-normalized hypergraph p-Laplacian semi-supervised learning methods. These methods will be applied to the zoo dataset and the tiny version of 20 newsgroups dataset. Experiment results show that the accuracy performance measures of these un-normalized hypergraph p-Laplacian based semi-supervised learning methods are significantly greater than the accuracy performance measure of the un-normalized hypergraph Laplacian based semi-supervised learning method (the current state of the art method hypergraph Laplacian based semi-supervised learning method for classification problem with p=2).
Most network-based speech recognition methods are based on the assumption that the labels of two adjacent speech samples in the network are likely to be the same. However, assuming the pairwise relationship between speech samples is not complete. The information a group of speech samples that show very similar patterns and tend to have similar labels is missed. The natural way overcoming the information loss of the above assumption is to represent the feature data of speech samples as the hypergraph. Thus, in this paper, the three un-normalized, random walk, and symmetric normalized hypergraph Laplacian based semi-supervised learning methods applied to hypergraph constructed from the feature data of speech samples in order to predict the labels of speech samples are introduced. Experiment results show that the sensitivity performance measures of these three hypergraph Laplacian based semi-supervised learning methods are greater than the sensitivity performance measures of the Hidden Markov Model method (the current state of the art method applied to speech recognition problem) and graph based semi-supervised learning methods (i.e. the current state of the art network-based method for classification problems) applied to network created from the feature data of speech samples.
Conference by Yann LeCun, computer scientist working in machine learning, computer vision, mobile robotics and computational neuroscience, who sees self-supervised learning as a potential solution for problems in reinforcement learning, as it has the advantage of taking both input and output as part of a complete system, making it effective for example in image completing, image transferring, time sequence data prediction, etc. While the model's complexity increases with the addition of feedback information, self-supervised learning models significantly reduce human involvement in the process.
Supervised learning – A blessing we have in this machines era. It helps to depict inputs to outputs. It uses labelled training data to deduce a function which has set of training examples. The majority of practical machine learning uses supervised learning as on date. AILabPage defines Machine Learning as "A focal point where business, data and experience meets emerging technology and decides to work together".
AI can get pretty good at creating content like images and videos, so researchers are trying to get them to design game levels. Machines are okay working on their own and can regurgitate the same material seen in the numerous training examples fed by its human creators. It's fine if what you're after is more of the same thing, but that's boring for games. Game designing bots need more creativity and the best place to learn is off humans. A team of researchers from the Georgia Institute of Technology conducted a series of experiments where humans partnered up with bots to come up with new levels in Super Mario, a popular Nintendo platform game.
I like to think of ML tasks as questions in a certain format, for which the system we're building gives answers. The question has to be about a certain "object" of the real world (which we call the input). In the supervised learning paradigm -- which we're focusing on in this series -- we would make the system learn from example objects AND from the answers for each of them. The inputs in those questions are an email and a property. The Data Sources listed in the LEARN part of the Canvas (see Part II) should provide information about these inputs.