Noisy PN learning is the problem of binary classification when training examples may be mislabeled (flipped) uniformly with noise rate rho1 for positive examples and rho0 for negative examples. We propose Rank Pruning (RP) to solve noisy PN learning and the open problem of estimating the noise rates, i.e. the fraction of wrong positive and negative labels. Unlike prior solutions, RP is time-efficient and general, requiring O(T) for any unrestricted choice of probabilistic classifier with T fitting time. We prove RP has consistent noise estimation and equivalent expected risk as learning with uncorrupted labels in ideal conditions, and derive closed-form solutions when conditions are non-ideal. RP achieves state-of-the-art noise estimation and F1, error, and AUC-PR for both MNIST and CIFAR datasets, regardless of the amount of noise and performs similarly impressively when a large portion of training examples are noise drawn from a third distribution. To highlight, RP with a CNN classifier can predict if an MNIST digit is a "one"or "not" with only 0.25% error, and 0.46 error across all digits, even when 50% of positive examples are mislabeled and 50% of observed positive labels are mislabeled negative examples.

In pattern recognition, the KNN algorithm is a method for classifying objects based on closest training examples in the feature space. KNN is a type of instance-based learning, or lazy learning where the function is only approximated locally and all computation is delayed until classification. The KNN is the fundamental and simplest classification technique when there is little or no prior knowledge about the distribution of the data. The K in KNN refers to number of nearest neighbors that the classifier will use to make its predication. In this video we use Game of Thrones example to explain kNN.

Whilst the use of machine learning in marketing seems to be skyrocketing, there's a dearth of coverage in the media that tries to get to the bottom of this technology in terms a layman can understand. I am irrefutably a layman, and have written a little on the topic of AI for Econsultancy, often specifically about ecommerce. In this blog post, we'll be talking to the founder of LoveTheSales.com, Before we start, let me point out that this year's Festival of Marketing has a whole stage (one of 12) dedicated to AI. View the agenda and get your tickets here. Machine learning is used to classify these products, tagging them to enable the website to sort them into the right categories and to show a user products they may be interested in.

What is supervised machine learning and how does it relate to unsupervised machine learning? In this post you will discover supervised learning, unsupervised learning and semis-supervised learning. Supervised and Unsupervised Machine Learning Algorithms Photo by US Department of Education, some rights reserved. The majority of practical machine learning uses supervised learning. Supervised learning is where you have input variables (x) and an output variable (Y) and you use an algorithm to learn the mapping function from the input to the output.

Recent work in learning ontologies (hierarchical and partially-ordered structures) has leveraged the intrinsic geometry of spaces of learned representations to make predictions that automatically obey complex structural constraints. We explore two extensions of one such model, the order-embedding model for hierarchical relation learning, with an aim towards improved performance on text data for commonsense knowledge representation. Our first model jointly learns ordering relations and non-hierarchical knowledge in the form of raw text. Our second extension exploits the partial order structure of the training data to find long-distance triplet constraints among embeddings which are poorly enforced by the pairwise training procedure. We find that both incorporating free text and augmented training constraints improve over the original order-embedding model and other strong baselines.

One of the things we haven't talked about much is the concept of human intervention when it comes to the dangers of artificial intelligence, especially in its early stages, where we are now with the technology. This may be far less of a "doomsday" or apocalyptic scenario, but the consequences could still be quite devastating on a person by person basis, or even affect larger groups depending on where a machine learning algorithm is deployed. We want to look at very specific cases where reinforcement learning is deployed with public access, much like how you can tell Google Translate that a translation is incorrect, and submit your improvements to them. Another good example would be marking an email that is not in your spam folder, but definitely belongs there, as such so the machine learning algorithm will become better over time. Exploiting these technologies can be done in a variety of ways, and while I initially thought it would take a large group to skew the learning of a machine by flooding it with many badly labeled training examples, it would not be impossible to have this done by some kind of botnet. See, most machine learning algorithms learn by training them on a so-called "labeled" data set, which is a large set of input data, and a label which is the desired perfect output of that input data.

In many cases, the acquisition of well-labelled training data is a huge hurdle for developing accurate prediction systems with supervised learning. At Love the Sales, we had the requirement to apply classification to the textual metadata of 2 million products (mostly fashion and homewares) into 1,000 different categories – represented in a hierarchy. In order to achieve this, we have architected a hierarchical tree of chained 2-class linear (Positive vs Negative) Support Vector Machines (LibSVM), each responsible for binary document classification of each hierarchical class. A key learning, is that the way in which these SVM's are structured can actually have a significant impact on how much training data has to be applied, for example, a naive approach would have been as follows: This approach requires that for every additional sub-category, two new SVM's be trained – for example, the addition of a new class for'Swimwear' would require an additional SVM under Men's and Women's – not to mention the potential complexity of adding a'Unisex' class at the top level. Overall, deep hierarchical structures can be too rigid to work with.

Language is increasingly being used to define rich visual recognition problems with supporting image collections sourced from the web. Structured prediction models are used in these tasks to take advantage of correlations between co-occurring labels and visual input but risk inadvertently encoding social biases found in web corpora. In this work, we study data and models associated with multilabel object classification and visual semantic role labeling. We find that (a) datasets for these tasks contain significant gender bias and (b) models trained on these datasets further amplify existing bias. For example, the activity cooking is over 33% more likely to involve females than males in a training set, and a trained model further amplifies the disparity to 68% at test time. We propose to inject corpus-level constraints for calibrating existing structured prediction models and design an algorithm based on Lagrangian relaxation for collective inference. Our method results in almost no performance loss for the underlying recognition task but decreases the magnitude of bias amplification by 47.5% and 40.5% for multilabel classification and visual semantic role labeling, respectively.

We propose and analyze a regularization approach for structured prediction problems. We characterize a large class of loss functions that allows to naturally embed structured outputs in a linear space. We exploit this fact to design learning algorithms using a surrogate loss approach and regularization techniques. We prove universal consistency and finite sample bounds characterizing the generalization properties of the proposed methods. Experimental results are provided to demonstrate the practical usefulness of the proposed approach.

One of the major hurdles preventing the full exploitation of information from online communities is the widespread concern regarding the quality and credibility of user-contributed content. Prior works in this domain operate on a static snapshot of the community, making strong assumptions about the structure of the data (e.g., relational tables), or consider only shallow features for text classification. To address the above limitations, we propose probabilistic graphical models that can leverage the joint interplay between multiple factors in online communities --- like user interactions, community dynamics, and textual content --- to automatically assess the credibility of user-contributed online content, and the expertise of users and their evolution with user-interpretable explanation. To this end, we devise new models based on Conditional Random Fields for different settings like incorporating partial expert knowledge for semi-supervised learning, and handling discrete labels as well as numeric ratings for fine-grained analysis. This enables applications such as extracting reliable side-effects of drugs from user-contributed posts in healthforums, and identifying credible content in news communities. Online communities are dynamic, as users join and leave, adapt to evolving trends, and mature over time. To capture this dynamics, we propose generative models based on Hidden Markov Model, Latent Dirichlet Allocation, and Brownian Motion to trace the continuous evolution of user expertise and their language model over time. This allows us to identify expert users and credible content jointly over time, improving state-of-the-art recommender systems by explicitly considering the maturity of users. This also enables applications such as identifying helpful product reviews, and detecting fake and anomalous reviews with limited information.