Do you get what overfitting means in machine learning? If you don't, then you better learn about it if you want to use or leverage machine learning. Because overfitting can ruin the effectiveness of machine learning. I wrote this blog because I found existing explanations of overfitting to be too technical. I hope this one is more consumable by non specialists. Machine learning involves a fairly complex workflow, see Machine Learning Algorithm!

Do you get what overfitting means in machine learning? If you don't, then you better learn about it if you want to use or leverage machine learning. Because overfitting can ruin the effectiveness of machine learning. I wrote this blog because I found existing explanations of overfitting to be too technical. I hope this one is more consumable by non specialists. Machine learning involves a fairly complex workflow, see Machine Learning Algorithm!

Looking in fine form after two days of rest, Roger Federer equaled Jimmy Connors' Open-era record by reaching his 14th Wimbledon quarterfinal and added to his own mark by making it at least that far at a Grand Slam tournament for the 48th time. Federer, a seven-time champion at the All England Club, has not dropped a set in the tournament through four matches after beating unseeded American Steve Johnson 6-2, 6-3, 7-5 at Centre Court on Monday. Johnson was making his debut in the fourth round of a major. The No. 3-seeded Federer hadn't played since Friday, when he was the only man to finish a third-round match. He next faces No. 9 Marin Cilic, the 2014 US Open champion, who advanced when Kei Nishikori retired from their fourth-round match.

Input: a) A set of labelled examples where every is the input vector, and is the corresponding output label. Output: The set of expected labels for all instances in . There are two ways (or rather, two philosophies) you could use, to solve this problem. Induction, in the context of learning, is the attempted discovery of rules/generalizations based on analysis of collected data. 'Attempted discovery' is the key term here – the generalizations are not facts, but approximations based on evidence you have gathered.

Community-based Question Answering (CQA) sites play an important role in addressing health information needs. However, a significant number of posted questions remain unanswered. Automatically answering the posted questions can provide a useful source of information for online health communities. In this study, we developed an algorithm to automatically answer health-related questions based on past questions and answers (QA). We also aimed to understand information embedded within online health content that are good features in identifying valid answers. Our proposed algorithm uses information retrieval techniques to identify candidate answers from resolved QA. In order to rank these candidates, we implemented a semi-supervised leaning algorithm that extracts the best answer to a question. We assessed this approach on a curated corpus from Yahoo! Answers and compared against a rule-based string similarity baseline. On our dataset, the semi-supervised learning algorithm has an accuracy of 86.2%. UMLS-based (health-related) features used in the model enhance the algorithm's performance by proximately 8 %. A reasonably high rate of accuracy is obtained given that the data is considerably noisy. Important features distinguishing a valid answer from an invalid answer include text length, number of stop words contained in a test question, a distance between the test question and other questions in the corpus as well as a number of overlapping health-related terms between questions. Overall, our automated QA system based on historical QA pairs is shown to be effective according to the data set in this case study. It is developed for general use in the health care domain which can also be applied to other CQA sites.

We are interested in estimating individual labels given only coarse, aggregated signal over the data points. In our setting, we receive sets ("bags") of unlabeled instances with constraints on label proportions. We relax the unrealistic assumption of known label proportions, made in previous work; instead, we assume only to have upper and lower bounds, and constraints on bag differences. We motivate the problem, propose an intuitive formulation and algorithm, and apply our methods to real-world scenarios. Across several domains, we show how using only proportion constraints and no labeled examples, we can achieve surprisingly high accuracy. In particular, we demonstrate how to predict income level using rough stereotypes and how to perform sentiment analysis using very little information. We also apply our method to guide exploratory analysis, recovering geographical differences in twitter dialect.

Computer vision systems typically learn how to recognize an object by analyzing images of thousands of examples. But scientists at Disney Research have shown that computers also can learn to recognize objects they have never seen before, based in part on studying vocabulary. People, after all, can get an idea of what things might look like based on reading a book. Similarly, a computer that already has been taught to recognize certain objects - apples, for instance - can analyze word use to get hints about the existence of fruits such as pears and peaches, and how they might differ from apples, said Leonid Sigal, senior research scientist at Disney Research. The knowledge that other fruits exist also is helpful in teaching the computer about important characteristics of apples themselves, he added.

We introduce structured prediction energy networks (SPENs), a flexible framework for structured prediction. A deep architecture is used to define an energy function of candidate labels, and then predictions are produced by using back-propagation to iteratively optimize the energy with respect to the labels. This deep architecture captures dependencies between labels that would lead to intractable graphical models, and performs structure learning by automatically learning discriminative features of the structured output. One natural application of our technique is multi-label classification, which traditionally has required strict prior assumptions about the interactions between labels to ensure tractable learning and prediction. We are able to apply SPENs to multi-label problems with substantially larger label sets than previous applications of structured prediction, while modeling high-order interactions using minimal structural assumptions. Overall, deep learning provides remarkable tools for learning features of the inputs to a prediction problem, and this work extends these techniques to learning features of structured outputs. Our experiments provide impressive performance on a variety of benchmark multi-label classification tasks, demonstrate that our technique can be used to provide interpretable structure learning, and illuminate fundamental trade-offs between feed-forward and iterative structured prediction.

The learning that is being done is always based on some sort of observations or data, such as examples, direct experience, or instruction. For instance, you might wish to predict how much a user Bob will like a movie that he hasn't seen, based on her ratings of movies that he has seen. This means making informed guesses about some unobserved property of some object, based on observed properties of that object. Supervised learning is a type of machine learning algorithm that uses a known dataset (called the training dataset) to make predictions. The training dataset includes input data and response values.

Machine learning algorithms are optimized to model statistical properties of the training data. If the input data reflects stereotypes and biases of the broader society, then the output of the learning algorithm also captures these stereotypes. In this paper, we initiate the study of gender stereotypes in {\em word embedding}, a popular framework to represent text data. As their use becomes increasingly common, applications can inadvertently amplify unwanted stereotypes. We show across multiple datasets that the embeddings contain significant gender stereotypes, especially with regard to professions. We created a novel gender analogy task and combined it with crowdsourcing to systematically quantify the gender bias in a given embedding. We developed an efficient algorithm that reduces gender stereotype using just a handful of training examples while preserving the useful geometric properties of the embedding. We evaluated our algorithm on several metrics. While we focus on male/female stereotypes, our framework may be applicable to other types of embedding biases.