A pattern recognition system includes a learning unit, a learning unit, a threshold calculation unit, and a determining unit. The learning unit learns, based on learned data of a first pattern, a model for determining whether recognition object data is the first pattern. The learning unit calculates likelihood indicating how likely the recognition object data is the first pattern by using the model learned by the learning unit. The threshold calculation unit calculates a threshold to be compared with the likelihood to determine whether the recognition object data is the first pattern, based on first likelihood that is calculated with respect to learned data of the first pattern and second likelihood that is calculated with respect to learned data of a second pattern. The determining unit determines whether the recognition object data is the first pattern by using the threshold.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A pattern recognition system comprising: a learning unit to learn, based on learned data of a first pattern, a model for determining whether recognition object data is the first pattern; a likelihood calculation unit to calculate likelihood indicating how likely the recognition object data is the first pattern by using the model learned by the learning unit; a threshold calculation unit to calculate a threshold to be compared with the likelihood to determine whether the recognition object data is the first pattern, based on first likelihood that is calculated with respect to learned data of the first pattern and second likelihood that is calculated with respect to learned data of a second pattern; and a determining unit to determine whether the recognition object data is the first pattern by using the threshold. 2. The pattern recognition system according to claim 1, wherein the learning unit learns the model based on a plurality of pieces of learned data of the first pattern classified into any one of a plurality of categories, and the threshold calculation unit calculates the threshold for each of the categories. 3. The pattern recognition system according to claim 1, wherein the threshold calculation unit calculates the threshold having a value between a first value and a second value, the first value having highest frequency of a plurality of values of first likelihood calculated with respect to a plurality of pieces of learned data of the first pattern, and the second value having highest frequency of a plurality of values of second likelihood calculated with respect to a plurality of pieces of learned data of the second pattern. 4. The pattern recognition system according to claim 1, wherein the threshold calculation unit calculates the threshold having a value of an intersection of distribution of a plurality of values of first likelihood calculated with respect to a plurality of pieces of learned data of the first pattern and distribution of a plurality of values of second likelihood calculated with respect to a plurality of pieces of learned data of the second pattern. 5. The pattern recognition system according to claim 1, wherein the threshold calculation unit calculates the threshold having a specified value among values between a first value and a second value, the first value having highest frequency of a plurality of values of first likelihood calculated with respect to a plurality of pieces of learned data of the first pattern, and the second value having highest frequency of a plurality of values of second likelihood calculated with respect to a plurality of pieces of learned data of the second pattern. 6. The pattern recognition system according to claim 1, wherein the first pattern is a pattern of abnormal sound, the second pattern is a pattern of normal sound, and the threshold calculation unit calculates the threshold having a value determined in accordance with detection sensitivity specified as sensitivity in detecting the abnormal sound, among values between a first value and a second value, the first value having highest frequency of a plurality of values of first likelihood calculated with respect to a plurality of pieces of learned data of the first pattern, and the second value having highest frequency of a plurality of values of second likelihood calculated with respect to a plurality of pieces of learned data of the second pattern. 7. The pattern recognition system according to claim 1, wherein the first pattern is a pattern of abnormal sound, the second pattern is a pattern of normal sound, and the threshold calculation unit calculates the threshold having a value determined in accordance with a degree of danger specified as a degree of danger of the abnormal sound, among values between a first value and a second value, the first value having highest frequency of a plurality of values of first likelihood calculated with respect to a plurality of pieces of learned data of the first pattern, and the second value having highest frequency of a plurality of values of second likelihood calculated with respect to a plurality of pieces of learned data of the second pattern. 8. A computer program product comprising a non-transitory computer-readable medium including programmed instructions, the instructions causing a computer to function as: a learning unit to learn, based on learned data of a first pattern, a model for determining whether recognition object data is the first pattern; a likelihood calculation unit to calculate likelihood indicating how likely the recognition object data is the first pattern by using the model learned by the learning unit; a threshold calculation unit to calculate a threshold to be compared with the likelihood to determine whether the recognition object data is the first pattern, based on first likelihood that is calculated with respect to learned data of the first pattern and second likelihood that is calculated with respect to learned data of a second pattern; and a determining unit to determine whether the recognition object data is the first pattern by using the threshold. 9. The computer program product according to claim 8, wherein the learning unit learns the model based on a plurality of pieces of learned data of the first pattern classified into any one of a plurality of categories, and the threshold calculation unit calculates the threshold for each of the categories. 10. The computer program product according to claim 8, wherein the threshold calculation unit calculates the threshold having a value between a first value and a second value, the first value having highest frequency of a plurality of values of first likelihood calculated with respect to a plurality of pieces of learned data of the first pattern, and the second value having highest frequency of a plurality of values of second likelihood calculated with respect to a plurality of pieces of learned data of the second pattern. 11. The computer program product according to claim 8, wherein the threshold calculation unit calculates the threshold having a value of an intersection of distribution of a plurality of values of first likelihood calculated with respect to a plurality of pieces of learned data of the first pattern and distribution of a plurality of values of second likelihood calculated with respect to a plurality of pieces of learned data of the second pattern. 12. The computer program product according to claim 8, wherein the threshold calculation unit calculates the threshold having a specified value among values between a first value and a second value, the first value having highest frequency of a plurality of values of first likelihood calculated with respect to a plurality of pieces of learned data of the first pattern, and the second value having highest frequency of a plurality of values of second likelihood calculated with respect to a plurality of pieces of learned data of the second pattern. 13. The computer program product according to claim 8, wherein the first pattern is a pattern of abnormal sound, the second pattern is a pattern of normal sound, and the threshold calculation unit calculates the threshold having a value determined in accordance with detection sensitivity specified as sensitivity in detecting the abnormal sound, among values between a first value and a second value, the first value having highest frequency of a plurality of values of first likelihood calculated with respect to a plurality of pieces of learned data of the first pattern, and the second value having highest frequency of a plurality of values of second likelihood calculated with respect to a plurality of pieces of learned data of the second pattern. 14. The computer program product according to claim 8, wherein the first pattern is a pattern of abnormal sound, the second pattern is a pattern of normal sound, and the threshold calculation unit calculates the threshold having a value determined in accordance with a degree of danger specified as a degree of danger of the abnormal sound, among values between a first value and a second value, the first value having highest frequency of a plurality of values of first likelihood calculated with respect to a plurality of pieces of learned data of the first pattern, and the second value having highest frequency of a plurality of values of second likelihood calculated with respect to a plurality of pieces of learned data of the second pattern. 15. A pattern recognition method comprising: learning, based on learned data of a first pattern, a model for determining whether recognition object data is the first pattern; calculating likelihood indicating how likely the recognition object data is the first pattern by using the model learned in the learning; calculating a threshold to be compared with the likelihood to determine whether the recognition object data is the first pattern, based on first likelihood that is calculated with respect to learned data of the first pattern and second likelihood that is calculated with respect to learned data of a second pattern; and determining whether the recognition object data is the first pattern by using the threshold.