Output Hypotheses Research Articles

Upper Bounds on the Generalization Error of Private Algorithms for Discrete Data

In this work, we study the generalization capability of algorithms from an information-theoretic perspective. It has been shown that the expected generalization error of an algorithm is bounded from above by a function of the relative entropy between the conditional probability distribution of the algorithm's output hypothesis, given the dataset with which it was trained, and its marginal probability distribution. We build upon this fact and introduce a mathematical formulation to obtain upper bounds on this relative entropy. Assuming that the data is discrete, we then develop a strategy using this formulation, based on the method of types and typicality, to find explicit upper bounds on the generalization error of stable algorithms, i.e., algorithms that produce similar output hypotheses given similar input datasets. In particular, we show the bounds obtained with this strategy for the case of $\epsilon$-DP and $\mu$-GDP algorithms.

Sequential plan recognition: An iterative approach to disambiguating between hypotheses

Plan recognition algorithms output hypotheses about an agent's plans from its observed actions. Due to imperfect knowledge about the agent's behavior and the environment, it is often the case that there are multiple hypotheses about an agent's plans that are consistent with the observations, though only one of these hypotheses is correct. This paper addresses the problem of how to disambiguate between hypotheses during the recognition process, by querying the acting agent about whether a given plan is part of the correct hypothesis. The main contribution is a sound and complete process for reducing the set of possible hypotheses called Sequential Plan Recognition (SPR). SPR iteratively queries the user and revises the set of possible hypotheses according to the outcome of the query. Several policies are provided for choosing which plans to query the agent. These policies address the problem of how to reduce the number of hypotheses during the recognition process using a minimal number of queries. The proposed policies include policies that use maximum likelihood and information gain measures.The paper provides a complexity analysis of the SPR process and the proposed query policies. It demonstrate its efficiency on two known domains from the literature, describing how performance and runtime are affected by features in the domain. Our results can inform the design of future plan recognition systems that interleave the recognition process with intelligent interventions of their users.

Cursive Arabic Handwriting Recognition System Without Explicit Segmentation Based on Hidden Markov Models

In this paper we present a system for offline recognition cursive Arabic handwritten text which is analytical without explicit segmentation based on Hidden Markov Models (HMMs). Extraction features preceded by baseline estimation are statistical and geometric to integrate both the peculiarities of the text and the pixel distribution characteristics in the word image. These features are modelled using hidden Markov models. The HMM-based classifiercontains a training module and a recognition module. The training module estimates the parameters of each of the character HMMs uses the Baum-Welchalgorithm. In the recognition phase, feature vectors extracted from an image are passed to a network of word lexicon entries formed of character models. The character sequence providing the maximumlikelihood identifies the recognized entry. If required, the recognition can generate N best output hypotheses rather than just the single best one. To determine the best output hypotheses, the Viterbi algorithm is used.The experiments on images of the benchmark IFN/ENIT database show that the proposed system improves recognition.

Revisiting Input and Output Hypotheses in Second Language Learning

This article discusses the impact that both input and output hypotheses have on second language learning. For decades, there have been opposing ideas in this regard. Several studies were consulted to have a clearer view of which approach is more recommendable for people involved in language teaching. For several years teachers of second language strongly believed that learning a new language depended on the exposition of learners to the target language. For effective learning, the exposition should be higher than the students’ level but comprehensible enough for them to understand. However, this hypothesis was rejected when a new study revealed that the exposition was not enough. Students need to use the language they have learnt. These new ideas emerged from the output hypothesis that had a significant impact on the field of language teaching. The article concludes that both approaches can be used complementarily although more study is still needed. atology and expand its studies from macro-studies to micro-studies.<p> </p>

Output Strategies for English‐Language Learners: Theory to Practice

Language production, or output, is not simply a product to demonstrate learning but part of the learning process. The output hypothesis, a theoretical model of second‐language acquisition, proposes that second‐language learners must produce the language they are learning in order to obtain a level of proficiency similar to that of native speakers.The purpose of this article is to apply the principles of the output hypothesis to language and literacy instruction for English‐language learners (ELLs). A brief review of literature related to the input and output hypotheses is presented and is followed by application of the output theory to several common classroom practices. Emphasis is placed on the importance of moving beyond providing input for ELLs and intentionally targeting children's language production. Hypothetical examples of teacher talk and teaching strategies that encourage output are provided.

On the data consumption benefits of accepting increased uncertainty

In the context of learning paradigms of identification in the limit, we address the question: why is uncertainty sometimes desirable? We use mind change bounds on the output hypotheses as a measure of uncertainty and interpret ‘desirable’ as reduction in data memorization, also defined in terms of mind change bounds. The resulting model is closely related to iterative learning with bounded mind change complexity, but the dual use of mind change bounds — for hypotheses and for data — is a key distinctive feature of our approach. We show that situations exist where the more mind changes the learner is willing to accept, the less the amount of data it needs to remember in order to converge to the correct hypothesis. We also investigate relationships between our model and learning from good examples, set-driven, monotonic and strong-monotonic learners, as well as class-comprising versus class-preserving learnability.

Measuring Price Responsiveness Under a Profit Maximization Framework

The distinguishing feature of the study is in using a globally flexible functional form that permits one to calculate different types of elasticities under both constant and variable output hypotheses. The Symmetric Generalized McFadden cost function alongwith the output supply condition form the basis of the econometric model. To measure inputsubstitutabilities, we used Alien-Uzawa, Morishima, and Shadow elasticities of substitution. Empirical results, based on 300 farm households from West Bengal, India, show that fertilizer is most price sensitive input. It is a gross substitute for manure and human labor but complement to bullock labor. Manure is a gross substitute for all the inputs whereas human labor is gross complement to bullock labor. Using the Morishima measure we find that the fertilizer and bullock labor are complements when the price of the latterchanges. Similar complementary relationship is found between bullock and human labor due to changes in the price of human labor. All other inputs are Morishima substitutes. The Shadow elasticity of substitution estimates indicate that all factors are substitutes. The estimate of returns to scale indicates the presence of diminishing returns to scale.

Exact Learning Boolean Functions via the Monotone Theory

We study the learnability of boolean functions from membership and equivalence queries. We develop the Monotone Theory that proves (1) Any boolean function is learnable in polynomial time in its minimal disjunctive normal form size, its minimal conjunctive normal form size, and the number of variables n. In particular, (2) Decision trees are learnable. Our algorithms are in the model of exact learning with membership queries and unrestricted equivalence queries. The hypotheses to the equivalence queries and the output hypotheses are depth 3 formulas.