Optimization of average precision with Maximal Figure-of-Merit Learning

2011 ◽  
Author(s):  
Ilseo Kim ◽  
Chin-Hui Lee
Keyword(s):  
Figure Of Merit ◽  
Average Precision

Seti Figure of Merit

1997 ◽  
Vol 161 ◽  
pp. 711-717 ◽  
Author(s):  
John W. Dreher ◽  
D. Kent Cullers
Keyword(s):  
Figure Of Merit ◽  
Qualitative Difference ◽  
Current Generation ◽  
Detection Range ◽  
Sky Surveys ◽  
Independent Observations ◽  
Good For

AbstractWe develop a figure of merit for SETI observations which is anexplicitfunction of the EIRP of the transmitters, which allows us to treat sky surveys and targeted searches on the same footing. For each EIRP, we calculate the product of terms measuring the number of stars within detection range, the range of frequencies searched, and the number of independent observations for each star. For a given set of SETI observations, the result is a graph of merit versus transmitter EIRP. We apply this technique to several completed and ongoing SETI programs. The results provide a quantitative confirmation of the expected qualitative difference between sky surveys and targeted searches: the Project Phoenix targeted search is good for finding transmitters in the 109to 1014W range, while the sky surveys do their best at higher powers. Current generation optical SETI is not yet competitive with microwave SETI.

The Measurement of Performance in Probabilistic Diagnosis IV. Utility Considerations in Therapeutics and Prognostics

1981 ◽  
Vol 20 (02) ◽  
pp. 80-96 ◽  
Author(s):  
J. D. F. Habbema ◽  
J. Hilden
Keyword(s):  
Decision Theory ◽  
Figure Of Merit ◽  
Acute Abdominal Pain ◽  
Theory Approach ◽  
Expected Loss ◽  
Patient Benefit ◽  
Evaluation Approach ◽  
Average Loss ◽  
Actual Decision ◽  
Probabilistic Diagnosis

It is argued that it is preferable to evaluate probabilistic diagnosis systems in terms of utility (patient benefit) or loss (negative benefit). We have adopted the provisional strategy of scoring performance as if the system were the actual decision-maker (not just an aid to him) and argue that a rational figure of merit is given by the average loss which patients would incur by having the system decide on treatment, the treatment being selected according to the minimum expected loss principle of decision theory.A similar approach is taken to the problem of evaluating probabilistic prognoses, but the fundamental differences between treatment selection skill and prognostic skill and their implications for the assessment of such skills are stressed. The necessary elements of decision theory are explained by means of simple examples mainly taken from the acute abdomen, and the proposed evaluation tools are applied to Acute Abdominal Pain data analysed in our previous papers by other (not decision-theoretic) means. The main difficulty of the decision theory approach, viz. that of obtaining good medical utility values upon which the analysis can be based, receives due attention, and the evaluation approach is extended to cover more realistic situations in which utility or loss values vary from patient to patient.

Proteins and direct methods

1991 ◽  
Vol 197 (3-4) ◽  
Author(s):  
Fan Hai-fu ◽  
Hao Quan ◽  
M. M. Woolfson
Keyword(s):  
First Principles ◽  
Figure Of Merit ◽  
Direct Methods ◽  
Large Structure ◽  
Low Resolution ◽  
Figures Of Merit ◽  
Large Structures ◽  
Resolution Data

AbstractConventional direct methods, which work so well for small structures, are less successful for macromolecules. Where it has been demonstrated that a solution might be found using direct methods it is then found that the usual figures of merit are unable to distinguish the few good sets of phases from the large number of sets generated. The reasons for the difficulties with very large structures are considered from a first-principles approach taking into account both the factors of having a large number of atoms and low resolution data. A proposal is made for trying to recognize good phase sets by taking a large structure as a sum of a number of smaller structures for each of which a conventional figure of merit can be applied.

Energy Challenges and Optimization of Figure of Merit for Thermoelectric Materials

2014 ◽  
Vol 03 (10) ◽  
pp. 16475-16479
Author(s):  
ASHAQ HUSSAIN SOFI ◽  
BAASIT ABUBAKR ◽  
ANIL MAINI ◽  
MOHAMMAD ASHRAF SHAH
Keyword(s):  
Thermoelectric Materials ◽  
Figure Of Merit

Improving Document Retrieval with Spelling Correction for Weak and Fabricated Indonesian-Translated Hadith

2020 ◽  
Vol 4 (3) ◽  
pp. 551-557
Author(s):  
Muhammad zaky ramadhan ◽  
Kemas Muslim Lhaksmana
Keyword(s):  
Retrieval System ◽  
Islamic Law ◽  
Vector Space Model ◽  
Document Retrieval ◽  
The Internet ◽  
Average Precision ◽  
Spelling Correction ◽  
Space Model ◽  
The Mean ◽  
The Web

Hadith has several levels of authenticity, among which are weak (dhaif), and fabricated (maudhu) hadith that may not originate from the prophet Muhammad PBUH, and thus should not be considered in concluding an Islamic law (sharia). However, many such hadiths have been commonly confused as authentic hadiths among ordinary Muslims. To easily distinguish such hadiths, this paper proposes a method to check the authenticity of a hadith by comparing them with a collection of fabricated hadiths in Indonesian. The proposed method applies the vector space model and also performs spelling correction using symspell to check whether the use of spelling check can improve the accuracy of hadith retrieval, because it has never been done in previous works and typos are common on Indonesian-translated hadiths on the Web and social media raw text. The experiment result shows that the use of spell checking improves the mean average precision and recall to become 81% (from 73%) and 89% (from 80%), respectively. Therefore, the improvement in accuracy by implementing spelling correction make the hadith retrieval system more feasible and encouraged to be implemented in future works because it can correct typos that are common in the raw text on the Internet.

Aplikasi Deteksi Kemiripan Tugas Paper

2017 ◽  
Vol 15 (2) ◽  
pp. 5
Author(s):  
Anthony Anggrawan ◽  
Azhari
Keyword(s):  
Information Retrieval ◽  
Vector Space ◽  
Vector Space Model ◽  
Mean Average Precision ◽  
Average Precision ◽  
Information Searching ◽  
Space Model ◽  
Model Method

Information searching based on users’ query, which is hopefully able to find the documents based on users’ need, is known as Information Retrieval. This research uses Vector Space Model method in determining the similarity percentage of each student’s assignment. This research uses PHP programming and MySQL database. The finding is represented by ranking the similarity of document with query, with mean average precision value of 0,874. It shows how accurate the application with the examination done by the experts, which is gained from the evaluation with 5 queries that is compared to 25 samples of documents. If the number of counted assignments has higher similarity, thus the process of similarity counting needs more time, it depends on the assignment’s number which is submitted.

Pencarian Question-Answer Menggunakan Convolutional Neural Network Pada Topik Agama Berbahasa Indonesia

2018 ◽  
Vol 10 (1) ◽  
pp. 57-64 ◽  
Author(s):  
Rizqa Raaiqa Bintana ◽  
Chastine Fatichah ◽  
Diana Purwitasari
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Question Answering ◽  
Mean Average Precision ◽  
Average Precision ◽  
Community Based ◽  
Network Methods ◽  
The Mean ◽  
Index Terms ◽  
Search Information

Community-based question answering (CQA) is formed to help people who search information that they need through a community. One condition that may occurs in CQA is when people cannot obtain the information that they need, thus they will post a new question. This condition can cause CQA archive increased because of duplicated questions. Therefore, it becomes important problems to find semantically similar questions from CQA archive towards a new question. In this study, we use convolutional neural network methods for semantic modeling of sentence to obtain words that they represent the content of documents and new question. The result for the process of finding the same question semantically to a new question (query) from the question-answer documents archive using the convolutional neural network method, obtained the mean average precision value is 0,422. Whereas by using vector space model, as a comparison, obtained mean average precision value is 0,282. Index Terms—community-based question answering, convolutional neural network, question retrieval

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