Empirical risk minimization (ERM), with proper loss function and regularization, is the common practice of supervised classification. In this paper, we study training arbitrary (from linear to deep) binary classifier from only unlabeled (U) data by ERM but not by clustering in the geometric space. A two-step ERM is considered: first an unbiased risk estimator is designed, and then the empirical training risk is minimized. This approach is advantageous in that we can also evaluate the empirical validation risk, which is indispensable for hyperparameter tuning when some validation data is split from U training data instead of labeled test data. We prove that designing such an estimator is impossible given a single set of U data, but it becomes possible given two sets of U data with different class priors. This answers a fundamental question in weakly-supervised learning, namely what the minimal supervision is for training any binary classifier from only U data. Since the proposed learning method is based on unbiased risk estimates, the asymptotic consistency of the learned classifier is certainly guaranteed. Experiments demonstrate that the proposed method could successfully train deep models like ResNet and outperform state-of-the-art methods for learning from two sets of U data.

Lately, Generative Models are drawing a lot of attention. Much of that comes from Generative Adversarial Networks (GANs). Invented by Goodfellow et al, GANs are a framework in which two players compete with one another. The two actors, the generator G and discriminator D, are both represented by function approximators. Moreover, they play different roles in the game. Given a training data Dt, G creates samples as an attempt to mimic the ones from the same probability distribution as Dt.

For many interesting tasks, such as medical diagnosis and web page classification, a learner only has access to some positively labeled examples and many unlabeled examples. Learning from this type of data requires making assumptions about the true distribution of the classes and/or the mechanism that was used to select the positive examples to be labeled. The commonly made assumptions, separability of the classes and positive examples being selected completely at random, are very strong. This paper proposes a weaker assumption that assumes the positive examples to be selected at random, conditioned on some of the attributes. To learn under this assumption, an EM method is proposed. Experiments show that our method is not only very capable of learning under this assumption, but it also outperforms the state of the art for learning under the selected completely at random assumption.

In supervised learning, the machine is taught by example. The operator provides the machine learning algorithm with a known dataset that includes desired inputs and outputs, and the algorithm must find a method to determine how to arrive at those inputs and outputs. While the operator knows the correct answers to the problem, the algorithm identifies patterns in data, learns from observations and makes predictions. The algorithm makes predictions and is corrected by the operator – and this process continues until the algorithm achieves a high level of accuracy/performance. Semi-supervised learning is similar to supervised learning but instead uses both labelled and unlabelled data. Labelled data is essentially information that has meaningful tags so that the algorithm can understand the data, while unlabelled data lacks that information.

At first glance, portraits of the Belamy family seem to exemplify life in the upper echelons of French society. The haughty features of patriarch Le Comte De Belamy are framed by a voluminous white powdered wig, while the dynastic matriarch, La Comtesse, oozes wealth in her colorful silk attire. Skipping ahead several generations, you'll encounter Madame De Belamy, whose tightly coiffed hair is tucked inside a blue hat rendered in Impressionistic strokes, and her son Edmond, a comparatively dour-looking young man clad almost entirely in black. Although its members' names and places in the family tree were assigned by Obvious, a Paris-based art collective, their likenesses are the brainchild of Generative Adversarial Networks, a machine learning algorithm better known by the acronym GAN. Now, Naomi Rea writes for Artnet News, the youngest member of the family--as depicted in "Portrait of Edmond Belamy"--is set to make history as the subject of the first AI-produced artwork sold by an auction house.

Looking into what a generative adversarial network is to understand how they work. Before we even think about starting to talk about Generative Adversarial Networks (GANs), it is worth asking the question of what's in a generative model? Why do we even want to have such a thing? These questions can help seed our thought process to better engage with GANs. So why do we want a generative model?

The ever-increasing size of modern datasets combined with the difficulty of obtaining label information has made semi-supervised learning of significant practical importance in modern machine learning applications. Compared with supervised learning, the key difficulty in semi-supervised learning is how to make full use of the unlabeled data. In order to utilize manifold information provided by unlabeled data, we propose a novel regularization called the tangent-normal adversarial regularization, which is composed by two parts. The two terms complement with each other and jointly enforce the smoothness along two different directions that are crucial for semi-supervised learning. One is applied along the tangent space of the data manifold, aiming to enforce local invariance of the classifier on the manifold, while the other is performed on the normal space orthogonal to the tangent space, intending to impose robustness on the classifier against the noise causing the observed data deviating from the underlying data manifold. Both of the two regularizers are achieved by the strategy of virtual adversarial training. Our method has achieved state-of-the-art performance on semi-supervised learning tasks on both artificial dataset and FashionMNIST dataset.

Unsupervised learning may sound like a fancy way to say "let the kids learn on their own not to touch the hot oven" but it's actually a pattern-finding technique for mining inspiration from your data. It has nothing to do with machines running around without adult supervision, forming their own opinions about things. If this feels familiar, unsupervised machine learning might be your new best friend. This post is beginner-friendly, but assumes you're familiar with the story so far: Check out the six instances above. These photographs are not accompanied by labels.

"GANs are the secret weapon for AI companies and projects," TechRepublic contributor Jay Garmon said. TechRepublic's Dan Patterson spoke to Garmon to discuss how companies must use generative adversarial networks (GANs) if they want to succeed using artificial intelligence (AI). GANs are two different AI software packages that fight against each other in order improve one another. These networks became popular in 2014, primarily for use in video and image processing. SEE: IT leader's guide to the future of artificial intelligence (Tech Pro Research) "You tend to have two different AI systems in this pit fight: One is the generator and one is the discriminator," Garmon said.

In Unsupervised Learning; data have no target attribute. In this learning algorithm takes as training examples the set of attributes/features alone. This is our second post in this sub series "Machine Learning Types". Our master series for this sub series is "Machine Learning Explained". Unsupervised Learning; is one of three types of machine learning i.e. This post is limited to Unsupervised Machine Learning to explorer its details.